DETAILED ACTION
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “an acquisition model configured to acquire, a prediction module configured to predict, a calculation moule configured to calculate, and an outputting module configured to output” in claim 14.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The structure for these modules is disclosed in applicant’s filed specification in ¶71, “The processor 40 may be a central processing unit (CPU), a universal processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, or the like. The universal processor may be a microprocessor, or any conventional processor.”
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-7, 9, and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (CN 110163068 A) [see Google Translation] in view of Qiu et al. (CN 110163068 A) [see Google Translation].
Regarding claim 1, Xu discloses, an object tracking method based on image space positioning, comprising: acquiring first position coordinates of a plurality of object detection boxes at a current moment, the first position coordinates comprising first pixel coordinates and first world coordinates; predicting second position coordinates of a candidate box of each object detection box at the next moment on the basis of the first position coordinates; (See Xu p. 5, 5th to 8th para, “The historical frame image may be an image of a previous frame or an image of a previous frame, and is not limited herein. After the target of the previous frame is identified, the track of the target of the previous frame is predicted to obtain a predicted target at the current moment, … When the track is predicted, a tracking point is determined first, then a world coordinate system is established … The center point of the lower edge of the target frame output by the target detection algorithm may be taken as a tracking point.”
Further see Xu p. 6 3rd para, “Wherein, in calculating the overlap degree association relationship, IoU of the predicted box of the tracked upper target set and the measured box of the new target set can be calculated.”)
calculating multi-dimensional similarities between each object detection box and a plurality of candidate boxes on the basis of the second position coordinates and third position coordinates of the object detection box at the next moment, the multi-dimensional similarities comprising a multi-dimensional similarity (See Xu, p. 5, 9th to 11th para, “S220, associating the current target set with the prediction target set under multiple dimensions to obtain an association result; wherein each dimension represents an association between the current target set and the predicted target set. In the embodiment of the present invention, the association result is a matrix formed by association degrees corresponding to association relationships between the current target in the current target set and the predicted targets in the predicted target set. In some embodiments, the plurality of dimensions includes at least one of: distance incidence relation, overlapping degree incidence relation and similarity incidence relation.”)
and the third position coordinates being actual measured position coordinates; (See Xu p. 5, 4th para, “wherein the current target set comprises a plurality of data of the current target determined by the image of the current frame.”)
and outputting an object tracking result at the next moment by using a preset matching algorithm according to the multi-dimensional similarities. (See Xu p. 6, 4th to 5th para, “And S230, determining a matching result of the predicted target and the current target according to the correlation result. … For example, the correlation result may be traversed through the Hungarian algorithm, so as to obtain a matching result between the predicted target and the current target.”)
Xu discloses the above limitations, but he fails to disclose, determined on the basis of a dynamic threshold of a centroid distance.
However, Qiu discloses, determined on the basis of a dynamic threshold of a centroid distance, See Qiu p. 6 last para to p. 7, 1st para, “S208. From the plurality of candidate objects, exclude the candidate objects whose position comparison parameters meet the interference object determination condition.
Wherein, the interference object determination condition is a condition for judging that the candidate object is an interference object. When the objects to be compared include the target object, the interference object determination condition can specifically be that the distance between the candidate object and the target object exceeds a distance threshold, or the area overlap between the candidate object and the target object is less than a preset threshold, etc.”
Further see Qiu p. 7, 5th para, “Wherein, the distance threshold may be a predetermined fixed distance, or a dynamic distance, such as preset N times (for example, 1.5 times) the length of a certain side of the target object.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a dynamic distance threshold to exclude candidate objects as suggested by Qiu to Xu’s tracking of objects by matching. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is to exclude candidate bounding boxes during matching eliminates duplicate detections, and reduces computational cost in computer vision.
Regarding claim 2, Xu and Qiu disclose, the object tracking method based on image space positioning according to claim 1, wherein the calculating multi-dimensional similarities between each object detection box and a plurality of candidate boxes on the basis of the second position coordinates and third position coordinates of the object detection box at the next moment comprises:
screening, for each object detection box, at least one target candidate box corresponding to the object detection box from the plurality of candidate boxes (See Qiu p. 6 last para, “S208. From the plurality of candidate objects, exclude the candidate objects whose position comparison parameters meet the interference object determination condition.”)
by using a preset dynamic threshold of a centroid distance; Further see Qiu p. 7, 5th para, “Wherein, the distance threshold may be a predetermined fixed distance, or a dynamic distance, such as preset N times (for example, 1.5 times) the length of a certain side of the target object.”
Further see Qiu p. 7 4th para, “For example, the computer device may respectively determine the center point coordinates of the candidate object and the target object according to the location information, and calculate the distance between the candidate object and the target object according to the center point coordinates.”)
and calculating a multi-dimensional similarity between each object detection box and the target candidate box on the basis of the second position coordinates and the third position coordinates. (See Xu, p. 5, 9th para, “S220, associating the current target set with the prediction target set under multiple dimensions to obtain an association result; wherein each dimension represents an association between the current target set and the predicted target set.’)
Regarding claim 5, Xu and Qiu disclose, the object tracking method based on image space positioning according to claim 2, wherein the calculating the multi-dimensional similarities between each object detection box and the target candidate boxes on the basis of the second position coordinates and the third position coordinates comprises:
calculating a detection box centroid offset similarity between each object detection box and the target candidate box on the basis of third centroid pixel coordinates of the object detection box and second centroid pixel coordinates of the candidate boxes. (See Xu p. 5 12th para, “In the embodiment of the present invention, the distance association relationship may be a mahalanobis distance, a distance variance association relationship, and the like, which is not limited herein. The relationship of overlapping degree may be loU (Intersection over Union), GloU (Generalized lntersection - loU), DloU (Complete-IOU, taking into account the Intersection ratio of the center point Distance).”)
Regarding claim 6, Xu and Qiu disclose, the object tracking method based on image space positioning according to claim 5, wherein the calculating the multi-dimensional similarity between each object detection box and the target candidate box on the basis of the second position coordinates and the third position coordinates comprises:
calculating a detection box shape change similarity (See Xu p. 6, 3rd para, “Wherein, in calculating the overlap degree association relationship, loU of the predicted box of the tracked upper target set and the measured box of the new target set can be calculated, and the relationship value of loU is returned. If the loU pixel number is not zero, the correlation between the objects is returned. loU_association=loU_pix/max(w*h} Wherein loU_associationCorrelation coefficient of loU, loU_pix For the number of pixels contained in loU,max(w*h} Is the union of two associated object boxes. Wherein, wis the width of the target frame or frames his the height of the target box.”)
and a detection box area similarity between each object detection box and the target candidate box on the basis of third frame pixel coordinates of the object detection box and second frame pixel coordinates of the candidate boxes. (See Xu p. 5, 12th para, “The similarity correlation may be an aspect ratio similarity, a size similarity, and the like of the target frame, and is not limited herein.”)
Regarding claim 7, Xu and Qiu disclose, the object tracking method based on image space positioning according to claim 6, wherein the calculating the multi-dimensional similarity between each object detection box and the target candidate box on the basis of the second position coordinates and the third position coordinates comprises:
calculating a world coordinate offset similarity between each object detection box and the target candidate box on the basis of third world coordinates of the object detection box and second world coordinates of the candidate boxes. (See Xu p. 5, 6th para, “When the track is predicted, a tracking point is determined first, then a world coordinate system is established … The center point of the lower edge of the target frame output by the target detection algorithm may be taken as a tracking point.”
Further see Xu p. 5, 12th para, “In the embodiment of the present invention, the distance association relationship may be a mahalanobis distance, a distance variance association relationship, and the like, which is not limited herein. The relationship of overlapping degree may be loU (Intersection over Union), GloU (Generalized lntersection - loU), DloU (Complete-IOU, taking into account the Intersection ratio of the center point Distance).”)
Regarding claim 9, Xu and Qiu disclose, the object tracking method based on image space positioning according to claim 1, wherein the acquiring first position coordinates of a plurality of object detection boxes at a current moment comprises:
acquiring first pixel coordinates of the plurality of object detection boxes at the current moment; generating, for each object detection box, object grounding point coordinates of the object detection box on the basis of the first pixel coordinates; (See Xu p. 5, 5th to 8th para, “The historical frame image may be an image of a previous frame or an image of a previous frame, and is not limited herein. After the target of the previous frame is identified, the track of the target of the previous frame is predicted to obtain a predicted target at the current moment, … When the track is predicted, a tracking point is determined first, then a world coordinate system is established … The center point of the lower edge of the target frame output by the target detection algorithm may be taken as a tracking point.”
Further see Xu p. 6 3rd para, “Wherein, in calculating the overlap degree association relationship, IoU of the predicted box of the tracked upper target set and the measured box of the new target set can be calculated.”)
and calculating space coordinates of the object grounding point coordinates in a world space on the basis of calibrated intrinsic parameters and calibrated extrinsic parameters of the object tracking device, wherein the space coordinates are first world coordinates of the object detection boxes at the current moment. (See Xu p. 5, 8th para, “When the world coordinate system is established, the world coordinate of the point can be calculated according to the pixel coordinate of the tracking point. … Calculating a rotation translation matrix between camera coordinates and world coordinates according to camera calibration information, namely camera internal parameters and camera external parameters, and then establishing a projection relation between the world coordinates and pixel coordinates to obtain world coordinates of the target on a ground plane.”)
Regarding claim 11, Xu and Qiu disclose, the object tracking method based on image space positioning according to claim 1, wherein the predicting second position coordinates of a candidate box of each object detection box at the next moment on the basis of the first position coordinates comprises:
predicting, for each object detection box, the second position coordinates of the candidate box of the object detection box at the next moment by using a Kalman filtering algorithm according to the first position coordinates of the object detection box at the current moment and fourth position coordinates at the previous moment. (See Xu, p. 8, 6th para, “In some embodiments, after S230, the multi-target tracking matching method further includes: updating the parameters of a pre-established Kalman tracker according to the matching target; the Kalman tracker is used for predicting according to images of historical frames to obtain a prediction target set.”)
Regarding claim 12, Xu and Qiu disclose, the object tracking method based on image space positioning according to claim 11, wherein the Kalman filtering algorithm further comprises:
tuning parameters of a state transition matrix and process noise according to motion data of the object detection box; (See Xu p. 8, 7th para, “wherein, the formula (3) is a system equation,X k is the state quantity of the system, and the state quantity of the system,X k =[xyV x V y ] T and F is a state transition matrix,V k is systematic noise, which conforms to a normal distributionV k ~N(0，Q)。yAndxrespectively the ordinate and the abscissa of the object,V y in the case of a longitudinal speed, the speed,V x is the transverse velocity. The formula (4) is a system observation equation,W k to measure noise, it follows a normal distributionW k ~N(0，R).”)
and inputting the first position coordinates and the fourth position coordinates into a preset motion model for candidate box prediction at the next moment according to the tuned parameters, (See Xu equations 6 to 13. Also see Xu p. 10 2nd para, “In some embodiments, the multi-target tracking matching method further comprises: obtaining a matching result in a preset optimization period; performing residual error analysis on a pre-established Kalman tracker according to a matching result in a preset optimization period to obtain an analysis result; and optimizing parameters of the Kalman tracker according to the analysis result.”)
wherein the preset motion model adopts any one of a uniform velocity model, a random acceleration model, a steering model or a nonlinear module. (See Xu p. 8, 6th para, “In an embodiment of the invention, the Kalman tracking instrument model and algorithm are as follows. The tracking model is based on a uniform speed model and is modeled as follows.”)
Regarding claim 13, Xu and Qiu disclose, the object tracking method based on image space positioning according to claim 1, wherein the outputting an object tracking result at the next moment by using a preset matching algorithm according to the multi-dimensional similarities comprises:
matching a final target candidate box corresponding to each object detection box at the next moment by using a Hungary matching algorithm according to the multi-dimensional similarities between each object detection box and the plurality of candidate boxes; and outputting the final target candidate box as the object tracking result. (See Xu p. 6, 4th to 5th para, “And S230, determining a matching result of the predicted target and the current target according to the correlation result. … For example, the correlation result may be traversed through the Hungarian algorithm, so as to obtain a matching result between the predicted target and the current target.”)
Regarding claim 14, Xu discloses, an object tracking apparatus based on image space positioning, comprising:an acquisition module, (See Xu p. 12, 8th para, “As shown in fig. 8, a terminal device 8 according to an embodiment of the present invention is provided, where the terminal device 8 includes: a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and operable on the processor 80.”)
configured to acquire first position coordinates of a plurality of object detection boxes at a current moment, the first position coordinates comprising first pixel coordinates and first world coordinates; a prediction module, configured to predict second position coordinates of a candidate box of each object detection box at the next moment on the basis of the first position coordinates; (See Xu p. 5, 5th to 8th para, “The historical frame image may be an image of a previous frame or an image of a previous frame, and is not limited herein. After the target of the previous frame is identified, the track of the target of the previous frame is predicted to obtain a predicted target at the current moment, … When the track is predicted, a tracking point is determined first, then a world coordinate system is established … The center point of the lower edge of the target frame output by the target detection algorithm may be taken as a tracking point.”
Further see Xu p. 6 3rd para, “Wherein, in calculating the overlap degree association relationship, IoU of the predicted box of the tracked upper target set and the measured box of the new target set can be calculated.”)
a calculation module, configured to calculate multi-dimensional similarities between each object detection box and a plurality of candidate boxes on the basis of the second position coordinates and third position coordinates of each object detection box at the next moment, the multi-dimensional similarities comprising a multi-dimensional similarity (See Xu, p. 5, 9th to 11th para, “S220, associating the current target set with the prediction target set under multiple dimensions to obtain an association result; wherein each dimension represents an association between the current target set and the predicted target set. In the embodiment of the present invention, the association result is a matrix formed by association degrees corresponding to association relationships between the current target in the current target set and the predicted targets in the predicted target set. In some embodiments, the plurality of dimensions includes at least one of: distance incidence relation, overlapping degree incidence relation and similarity incidence relation.”)
and the third position coordinates being actual measured position coordinates; (See Xu p. 5, 4th para, “wherein the current target set comprises a plurality of data of the current target determined by the image of the current frame.”)
and an outputting module, configured to output an object tracking result at the next moment by using a preset matching algorithm according to the multi-dimensional similarities. (See Xu p. 6, 4th to 5th para, “And S230, determining a matching result of the predicted target and the current target according to the correlation result. … For example, the correlation result may be traversed through the Hungarian algorithm, so as to obtain a matching result between the predicted target and the current target.”)
However, Qiu discloses, determined on the basis of a dynamic threshold of a centroid distance, See Qiu p. 6 last para to p. 7, 1st para, “S208. From the plurality of candidate objects, exclude the candidate objects whose position comparison parameters meet the interference object determination condition.
Wherein, the interference object determination condition is a condition for judging that the candidate object is an interference object. When the objects to be compared include the target object, the interference object determination condition can specifically be that the distance between the candidate object and the target object exceeds a distance threshold, or the area overlap between the candidate object and the target object is less than a preset threshold, etc.”
Further see Qiu p. 7, 5th para, “Wherein, the distance threshold may be a predetermined fixed distance, or a dynamic distance, such as preset N times (for example, 1.5 times) the length of a certain side of the target object.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a dynamic distance threshold to exclude candidate objects as suggested by Qiu to Xu’s tracking of objects by matching. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is to exclude candidate bounding boxes during matching eliminates duplicate detections, and reduces computational cost in computer vision.
Regarding claim 15, Xu and Qiu disclose, a computer device, comprising a processor and a memory, the memory being configured to store a computer program therein, the computer program being executed by the processor to implement the object tracking method based on image space positioning according to claim 1. (See Xu p. 12, 8th para, “As shown in fig. 8, a terminal device 8 according to an embodiment of the present invention is provided, where the terminal device 8 includes: a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and operable on the processor 80. The processor 80, when executing the computer program 82, implements the steps in the various multi-target tracking matching method embodiments described above.”)
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (CN 110163068 A) [see Google Translation] in view of Qiu et al. (CN 110163068 A) [see Google Translation] and in further view of Tsunashima et al. (US Pub. No. 2021/0142501 A1).
Regarding claim 10, Xu discloses, the object tracking method based on image space positioning according to claim 9, but he fails to disclose, further comprising detecting the plurality of object detection boxes, which comprises: performing distortion correction on an image acquired by the object tracking device, performing object detection on the corrected image to generate the object detection boxes, and correcting the object detection boxes, wherein a deep learning algorithm is used for processing in the process of performing object detection on the corrected image.
However, Tsunashima discloses, further comprising detecting the plurality of object detection boxes, which comprises: performing distortion correction on an image acquired by the object tracking device, performing object detection on the corrected image to generate the object detection boxes, and correcting the object detection boxes, wherein a deep learning algorithm is used for processing in the process of performing object detection on the corrected image. (See Tsunashima ¶47, “The object detecting process involves detecting an object region in an image through learning. The learning in this case is deep learning, for example. The algorithm of object detection through deep learning may be SSD (Single Shot Multi Box Detector). Alternatively, the algorithm may be YOLO (You Only Look Once), R-CNN (Regions with CNN features), or the like. As long as algorithm is capable of object detection at high speed with high accuracy, the algorithm is not limited to those described above, and any algorithm other than those may be utilized. The object detection processing section 21 outputs the distortion-corrected left-viewpoint image and the result of object region detection.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the distortion correction and deep learning object detection as suggested by Tsunashima to Xu and Qiu’s object tracking. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because deep learning object detection combined with distortion correction overcomes the limitations of traditional, manual feature-extraction methods. Together, they enable robust, continuous, and highly accurate tracking in complex, real-world environments.
Allowable Subject Matter
Claims 3-4 and 8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 3, the object tracking method based on image space positioning according to claim 2, wherein the screening, for each object detection box, at least one target candidate box corresponding to the object detection box from the plurality of candidate boxes by using a preset dynamic threshold of a centroid distance comprises: calculating a first centroid distance between each object detection box and an object tracking device, and second centroid distances between each object detection box and the plurality of candidate boxes, on the basis of first centroid world coordinates of the object detection box and second centroid pixel coordinates of the candidate boxes; determining a dynamic threshold corresponding to the first centroid distance on the basis of a preset dynamic threshold table of centroid distances; and screening, for each object detection box, target candidate boxes having a second centroid distance from the object detection box being greater than the dynamic threshold, each object detection box corresponding to at least one of the target candidate boxes. (The disclosed prior art of record fails to disclose all the limitations of this claim.)
Regarding claim 4, this claims is objected to since it depends from objected to claim 3.
Regarding claim 8, the object tracking method based on image space positioning according to claim 7, wherein the calculating the multi-dimensional similarity between each object detection box and the target candidate box further comprises: performing calculation by using a multi-dimensional similarity weighting model, wherein the multi-dimensional similarity weighting model at least comprises a distance measurement algorithm, an edge feature matching algorithm and a structural similarity index algorithm; and weighting, for each object detection box, the detection box centroid offset similarity, the detection box shape change similarity, the detection box area similarity and the world coordinate offset similarity to obtain the multi-dimensional similarity. (The disclosed prior art of record fails to disclose all the limitations of this claim.)
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
Li et al. (US Pub. No. 2023/0126351 A1) A system and method for tracking an object comprising: an image input gateway arranged to receive a series of image frames from a stream of images, wherein the stream of images includes representations of the object being spatially displaced over the series of image frames; an object tracker arranged to process the series of image frames to track the object with a first object location process arranged to locate the object in the series of image frames; and, when upon the object is unable to be located by the first object location process in any of the image frames within the series of image frames, use a second object location process to locate the object in the image frames or subsequent image frames for tracking the object through the remaining series of image frames.
Li et al. (US Pub. No. 2024/0404084 A1) Disclosed are a target tracking method, a target tracking system and an electronic device. The target tracking method includes acquiring N frames of images sequentially, wherein N is an integer and N≥2. The target tracking method further includes: performing target detection on an N-th frame of image to generate a target area; performing optical flow calculation and Kalman Filter prediction on a target area in an (N−1)-th frame of image to generate an optical flow predicted target area and a Kalman Filter predicted target area in the N-th frame of image, respectively; performing a first matching between the target area in the N-th frame of image and the optical flow predicted target area in the N-th frame of image;
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/DAVID PERLMAN/Primary Examiner, Art Unit 2673
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