DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 27, 2026, has been entered.
Response to Amendment
Applicants Amendments filed on August 27, 2026, has been entered and made of record.
Currently pending Claim(s): 1-20
Independent Claim(s): 1, 9, 20
Amended Claim(s): 1, 5, 7, 9, 17, 20
Canceled Claim(s): 6, 16
Response to Arguments
This office action is responsive to the Applicant’s Arguments/Remarks Made in an Amendment
received on August 27, 2026.
In view of amendments filed on August 27, 2026, the Applicant has amended independent Claim 1 to recite the additional limitation of “transforming a plurality of characteristic pixel points of the plurality of 2D density maps from a 2D image coordinate system to a 3D coordinate system of the aggregated volume model according to a plurality of camera parameter information indicated by a plurality of image capturing data to form a plurality of voxel points of the aggregated volume model”. Originally, (in the claim set dated March 16, 2026) Claim 1 recited, “by the processing device, generating an aggregated volume model by projecting the plurality of 2D density maps according to a plurality of image capturing data: by the processing device, generating a three-dimensional (3D) density map according to the aggregated volume model”, and was rejected under Chan (US Pub No 2020/0387718), Zhang (Zhang, Qi; Chan, Antoni B, “3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels”, 2020), and Jiang et al. (US Pub No 2020/0242777).
As discussed in the following paragraphs, the combination of Chan, Zhang, and Jiang does not render obvious the newly amended claims. However, the Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. Upon conducting a new search, the Examiner argues that the newly amended Claim(s) 1, 9, and 20 is/are unpatentable over Chan, Zhang, Hu (B. Jiang, L. Hu et al., "Probabilistic Triangulation for Uncalibrated Multi-View 3D Human Pose Estimation," 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 2023, pp. 14804-14814), and Jiang, since Hu teaches transforming a plurality of characteristic pixel points.
In view of Applicant’s Arguments/Remarks filed on August 27, 2026, with respect to the claims, the Applicant explained (on Remarks pg. 12 paragraph 2-3) that Zhang fails to teach generating an aggregated volume model. The Applicant explained that Zhang teaches generating an aggregated model by “transforming the 3D prediction G from the 3D coordinate system into the 2D image coordinate system, rather than transforming the 2D density maps from the 2D image coordinate system into the 3D coordinate system”.
The Examiner agrees. Zhang teaches generating 2D density maps (see pg. 2, Section 1, “2D single-view features are extracted and then decoded to the 2D density map”), and generating a refined aggregated volume model by projecting 2D density maps (see pg. 12-13, Section 3.4, “The projection consistency between the projected 2D density maps and the 2D density map ground-truth is measured and used as part of the loss to further enhance the 3D counting performance”), and see pg. 7, Section 5, “The projection consistency measure between the 3D prediction and 2D density map ground-truth is studied and then utilized in the loss function to refine the 3D prediction further”), but fails to explicitly teach transforming a 2D characteristic point from a heat map to a 3D coordinate.
Thus, the Applicant’s amendment necessitated the new grounds of rejection presented in Office Action, and the independent claims are rejected under 35 USC 103 as being unpatentable over Chan, Zhang, Hu, and Jiang. Therefore, the rejection to the dependent claims 2-5, 7-8 and 10-15, and 17-19 are maintained.
Claim Rejections - 35 USC § 103
Claims 1-2, 7-11, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al. (US Pub No 2020/0387718), hereinafter Chan, in view of Zhang et al. (Zhang, Qi; Chan, Antoni B, “3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels”, 2020), in view of and further in view of Jiang et al. (US Pub No 2020/0242777), hereinafter Jiang.
As to Claim 1, Chan teaches an area information estimation method applicable to an area information estimation system (see paragraph [0001], “The present invention relates to a system and method for counting objects, and particularly, although not exclusively, to a system and method for counting number of people in an area”),
comprising a processing device (see Fig. 1, processing unit 102)
and a plurality of monitor devices (see Fig. 2, cameras 208)
and comprising: by the plurality of monitor devices, capturing a plurality of images of an area from different views (see paragraph [0026], “a plurality of cameras arranged to obtain a plurality of images representing the objects to be counted in a target area”);
generating a plurality of two-dimensional (2D) density maps of at least one target object in the area according to the plurality of image (see paragraph [0010], “In an embodiment of the first aspect, the step of generating the map for each of the plurality of images comprises the step of generating a plurality of view-level density maps representing a count of the objects in each of the scenes representing respective portions of the target area”).
Chan fails to teach by the processing device, generating an aggregated volume model transforming a plurality of characteristic pixel points of the plurality of 2D density maps from a 2D image coordinate system to a 3D coordinate system of the aggregated volume model according to a plurality of camera parameter information indicated by a plurality of image capturing data to form a plurality of voxel points of the aggregated volume model. Chan fails to further teach generating a 3D density map according to the aggregated volume model, and then calculating a number of the at least one target object according to the 3D density map. Instead, the 2D density maps are projected to the same 2D level of a 3D scene (see paragraph [0010]).
However, Zhang et al. teaches a method for obtaining a 3D density map which comprises obtaining multiple 2D density maps from 2D features (see pg. 2, Section 1, “2D single-view features are extracted and then decoded to the 2D density map”),
projecting the 2D features to obtain an initial aggregated model (see pg. 2, caption under Figure 2, “Single-view features are extracted and then projected to the 3D world on multiple height planes. The projected 3D features are concatenated and fused to output the 3D density map prediction”)
and then generating a refined aggregated volume model by projecting 2D density maps (see pg. 12-13, Section 3.4, “The projection consistency between the projected 2D density maps and the 2D density map ground-truth is measured and used as part of the loss to further enhance the 3D counting performance”), and see pg. 7, Section 5, “The projection consistency measure between the 3D prediction and 2D density map ground-truth is studied and then utilized in the loss function to refine the 3D prediction further”).
generating the 3D density map according to the aggregated volume model (see Zhang, Section 3, page 3, “3) 3D fusion and prediction: the projected multi-view 3D features are fused to predict the 3D density maps using 3D CNN layers”, and see Figure 2, page 2, where the 3D volume model created by projecting the features forms a 3D density map prediction).
Zhang further teaches calculating a number of the at least one target object according to the 3D density map (see Section 1, page 2, “we propose to use 3D projection and 3D feature fusion to perform the multi-view counting task”).
Zhang is combinable with Chan as both use image analysis to count objects. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Chan with the teachings of Zhang. The motivation for doing so would be to reduce errors in counting due to 2D-to-2D projecting. Zhang teaches in Section 1, page 2, “The disadvantage of the 2D-to-2D projection in (Zhang and Chan 2019) is that the features of the same person from different views may not line up correctly due to the approximation that all features come from the same height in the 3D world. Clearly this is not true for features extracted from the heads and feet of the people. To address this problem, in this paper, we propose to use 3D projection and 3D feature fusion to perform the multi-view counting task.”
Both Chan and Zhang fail to teach aggregated model is generated by transforming a plurality of characteristic pixel points of the plurality of 2D density maps from a 2D image coordinate system to a 3D coordinate system of the aggregated volume model according to a plurality of camera parameter information indicated by a plurality of image capturing data to form a plurality of voxel points of the aggregated volume model.
However, in an analogous art, Hu teaches a method for generating 3D data (see pg. 14804, “This paper presents a novel Probabilistic Triangulation module that can be embedded in a calibrated 3D human pose estimation method”) which comprises
capturing a plurality of images of an area from different views (see pg. 14806, Section 2.3, caption under Figure 1, “The inputs to the network are images from different views at the same moment”);
generating a plurality of two-dimensional (2D) density maps of at least one target object in the area according to the plurality of image (see pg. 14806, Section 2.3, caption under Figure 1, “The 2D human pose heatmaps are estimated from the images and used to initialize the camera pose distribution”, wherein the heatmaps are interpreted as the ‘density maps’)
generating an aggregated model by transforming a plurality of characteristic pixel points of the plurality of 2D density maps from a 2D image coordinate system to a 3D coordinate system of the aggregated volume model (see Formula 1, shown below, and see Figure 1, where a subset of pixel points of the 2D heat maps (density maps) are transformed into 3D coordinates).
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645
1711
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according to a plurality of camera parameter information indicated by a plurality of image capturing data (see pg. 14806, Section 2.3, caption under Figure 1, “ “Then, Monte Carlo sampling is performed iteratively and each sampled camera parameter is computed by reprojection to obtain an explicit approximation of the 3D heatmap”)
to form a plurality of voxel points of the aggregated volume model (see Figure 1, voxels formed in the ‘explicit approximation 3D heatmap’).
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689
863
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Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the pixel to voxel transformation taught by Hu with the area estimation method taught by Chan and Zhang. The motivation for doing so would be to create accurate aggregate volume models. Hu teaches on pg. 14812, Section 5, “it is possible to build the multi-view 3D human pose estimation system in wild scenes and achieve a high-accurate capture. In the wild scenes, the Probabilistic Triangulation is not affected by the spatial location changes and the number of cameras”. Thus, it would have been obvious to combine the pixel-to-voxel mapping taught by Hu with the teachings of Chan and Zhang.
Chan, Zhang, and Hu fail to teach that the monitor device itself generates a plurality of 2D density maps of at least one target object in the area according to the plurality of images. Instead, images are sent to a computing device, which contains a processor which generates 2D density maps (see Chan, paragraph [0063]).
However, Jiang teaches a processor which can create 2D density maps (see paragraph [0004]), and can be integrated within a camera (see paragraph [0061] “For example, the system 600 may comprise, or be comprised in, an apparatus, such as a mobile phone, smart phone, camera (e.g., OzO, closed circuit television, webcam), drone, self-driving vehicle, car, unmanned aerial vehicle, autonomous vehicle, and/or Internet of Things (IoT sensor, such as a traffic sensor, industrial sensor, and/or the like) to enable counting of objects, in accordance with some example embodiments”). Jiang is combinable with Chan, Zhang, and Hu since all four are from the analogous field of image analysis. Thus, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Jiang with Chan. The motivation for doing so would be to allow for the function of counting objects to be integrated into a wide variety of appliances. Thus, it would have been obvious to combine in the teachings of Jiang with Chan, Zhang, and Hu in order to obtain the invention as claimed in Claim 1.
As to Claim 2, Chan in view of Zhang, Hu, and Jiang teaches wherein generating the plurality of 2D density maps of the at least one target object according to the plurality of images comprises:
by the plurality of monitor devices (see Jiang, paragraph [0061]),
transforming the plurality of images into the plurality of 2D density maps by using a 2D neural network model (see Chan ,paragraph [0088], “A fully-convolutional network (denoted as FCN-7) may be used on each camera view to extract image feature maps or estimate a corresponding view-level density map. ”).
As to Claim 7, Chan in view of Zhang and Jiang fails to teach transforming the plurality of characteristic pixel points of the plurality of 2D density maps from the 2D image coordinate system to the 3D coordinate system according to the plurality of camera parameter information indicated by the plurality of image capturing data comprises: calculating a position of at least one of the plurality of characteristic pixel points of the plurality of 2D density maps in the aggregated volume model according to the plurality of image capturing data, so as to form at least one of the plurality of voxel points of the aggregated volume model.
However, Hu teaches generating a plurality of two-dimensional (2D) density maps of at least one target object in the area according to the plurality of image (see pg. 14806, Section 2.3, caption under Figure 1, “The 2D human pose heatmaps are estimated from the images and used to initialize the camera pose distribution”, wherein the heatmaps are interpreted as the ‘density maps’)
generating an aggregated model by transforming a plurality of characteristic pixel points of the plurality of 2D density maps from a 2D image coordinate system to a 3D coordinate system of the aggregated volume model (see Formula 1, shown below, and see Figure 1, wherein a subset of pixel points of the 2D heat maps (density maps) are transformed into 3D coordinates).
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645
1711
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according to a plurality of camera parameter information indicated by a plurality of image capturing data (see pg. 14806, Section 2.3, caption under Figure 1, “ “Then, Monte Carlo sampling is performed iteratively and each sampled camera parameter is computed by reprojection to obtain an explicit approximation of the 3D heatmap”)
to form a plurality of voxel points of the aggregated volume model (see Figure 1, voxels formed in the ‘explicit approximation 3D heatmap’) comprise
calculating a position of at least one of the plurality of characteristic pixel points of the plurality of 2D density maps in the aggregated volume model according to the plurality of image capturing data (see Figure 1, where the pixel points of the 2D density map and the corresponding image are used to calculate a position of points in the ‘explicit approximation 3D heatmap’)
so as to form at least one of the plurality of voxel points of the aggregated volume model (see Figure 1, voxels formed in the ‘explicit approximation 3D heatmap’).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the pixel to voxel transformation taught by Hu with the area estimation method taught by Chan and Zhang. The motivation for doing so would be to create accurate aggregate volume models. Thus, it would have bene obvious to combine the teachings of Hu with the teachings of Chan, Zhang, and Jiang in order to obtain the invention as claimed in Claim 7.
As to Claim 8, Chan in view of Zhang, Hu, and Jiang fails to explicitly teach transforming the aggregated volume model into the 3D density map by using a 3D neural network model. However, Zhang teaches that a 3D neural network can be used to generate a 3D density map (see Section 3, page 3, “3D fusion and prediction: the projected multi-view 3D features are fused to predict the 3D density maps using 3D CNN layers”, where CNN stands for convolutional neural network).
As to Claim 9, Claim 9 recites a system, which performs the same method disclosed in Claim 1. Thus, the rejection and rationale are analogous to that made in Claim 1.
As to Claim 10, Chan teaches a plurality of monitor devices, each comprising a camera, configured to capture a corresponding one of the plurality of images (see paragraph [0026] “a plurality of cameras arranged to obtain a plurality of images representing the objects to be counted in a target area”).
Chan also teaches a processor (see Fig. 1, processing unit 102), configured to transform the corresponding one of the plurality of images into a corresponding one of the plurality of 2D density maps by using a 2D neural network model (see paragraph [0010], “In an embodiment of the first aspect, the step of generating the map for each of the plurality of images comprises the step of generating a plurality of view-level density maps representing a count of the objects in each of the scenes representing respective portions of the target area”).
Chan, Zhang, and Hu fail to teach a monitor where the processor and camera are directly coupled together. However, Jiang teaches a processor which can create density maps (see paragraph [0004]), and can be coupled to a camera (see paragraph [0061] “For example, the system 600 may comprise, or be comprised in, an apparatus, such as a mobile phone, smart phone, camera (e.g., OzO, closed circuit television, webcam), drone, self-driving vehicle, car, unmanned aerial vehicle, autonomous vehicle, and/or Internet of Things (IoT sensor, such as a traffic sensor, industrial sensor, and/or the like) to enable counting of objects, in accordance with some example embodiments”). Thus, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Jiang with Chan, Zhang, and Hu. The motivation for doing so would be to allow for counting objects to be integrated into a wide variety of appliances. Thus, it would have been obvious to combine in the teachings of Jiang with the method taught by Chan, Zhang, and Hu in order to obtain the invention as claimed in Claim 10.
As to Claim 11, Chan in view of Zhang, Hu, and Jiang teaches that the 2D neural network model is a convolutional neural network (see Chan, paragraph [0088], “A fully-convolutional network (denoted as FCN-7) may be used on each camera view to extract image feature maps or estimate a corresponding view-level density map”).
As to Claim 17, Claim 17 claims the same limitation claimed as Claim 7 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 7.
As to Claim 18, Claim 18 claims the same limitation claimed as Claim 8 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 8.
As to Claim 19, Chan fails to explicitly teach the 3D neural network model is a convolutional neural network. However, Zhang teaches a 3D CNN can be used to transform an aggregated volume model into a 3D density map (see Section 3, page 3, “3) 3D fusion and prediction: the projected multi-view 3D features are fused to predict the 3D density maps using 3D CNN layers”, where CNN stands for convolutional neural network).
As to Claim 20, Claim 20 claims a non-transitory computer readable storage medium with a computer program (see Chan, paragraph [0134]) that when executed performs the same process disclosed in Claim 1. Therefore, the rejection and rationale are analogous to that made in Claim 1.
Claims 3-5 and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al. (US Pub No 2020/0387718), hereinafter Chan, in view of Zhang et al. (Zhang, Qi; Chan, Antoni B, “3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels”, 2020), in view of Hu et al. (B. Jiang, L. Hu et al., "Probabilistic Triangulation for Uncalibrated Multi-View 3D Human Pose Estimation," 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 2023, pp. 14804-14814) further in view of Jiang et al. (US Pub No 2020/0242777), hereinafter Jiang, and further in view of Yim et al. (US Pub No 2021/0374431), hereinafter Yim.
As to Claim 3, Chan in view of Zhang, Hu, and Jiang fails to teach by the plurality of monitor devices, providing a plurality of image capturing data corresponding to the plurality of images to the processing device. Hu teaches that camera parameters may be estimated (see pg. 14807, Section 3.2, “Camera pose distribution approximation”), but fails to teach that the monitor itself provides these parameters.
However, Yim teaches device pose information can be obtained (see paragraph [0030], “Through an image calibration process of the camera, an intrinsic parameter and an extrinsic parameter may be obtained. The intrinsic parameter refers to internal information of the camera, such as structural errors of the camera caused by lens distortion or the like. The extrinsic parameter refers to information on how much the actually installed camera is moved from the origin of the world coordinate system and how much the camera is rotated”), and that this parameter information can be provided to processing units to perform distance calculation functions (see paragraph [0037], “According to another aspect, heads of two persons may be detected from the image captured by the camera, and a distance between the two persons may be calculated using a distance between positions of the heads on a plane corresponding to an average height using the camera parameters. In the video surveillance apparatus according to the aspect, the person-to-person distance calculation unit 100 may include a head-to-head distance calculation unit 150”).
Yim is combinable with Chan, Zhang, Hu and Jiang, since all five are from the analogous field of image analysis. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Yim with the teachings of Chan, Zhang , Hu and Jiang. The motivation for doing so would be to better estimate the positions objects within images by using camera parameters. Yim teaches in paragraph [0024], “ When camera parameters are given, absolute coordinate information corresponding to an absolute distance may be obtained.” Thus, it would have been obvious to combine the teachings of Yim with the teachings of Chan, Jiang and Zhang in order to obtain the invention as claimed in Claim 3.
As to Claim 4, Chan in view of Zhang , Hu and Jiang fails to teach when at least one of the plurality of monitor devices is moved, the area information estimation method further comprises: by the at least one of the plurality of monitor devices, using a visual-based localization technology to calculate at least one device pose information so as to generate at least one of the plurality of image capturing data.
However, Yim teaches that device pose information can be obtained after a camera is moved (see paragraph [0030], “Through an image calibration process of the camera, an intrinsic parameter and an extrinsic parameter may be obtained. The intrinsic parameter refers to internal information of the camera, such as structural errors of the camera caused by lens distortion or the like. The extrinsic parameter refers to information on how much the actually installed camera is moved from the origin of the world coordinate system and how much the camera is rotated” and paragraph [0031], “ a camera image calibration technique based on a homography is proposed”, where homograph is used as a visual-based localization technology).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Yim with the teachings of Chan, Zhang, Hu, and Jiang. The motivation for doing so would be to better estimate the positions objects within images by using camera parameters. Yim teaches in paragraph [0024], “ When camera parameters are given, absolute coordinate information corresponding to an absolute distance may be obtained.” Thus, it would have been obvious to combine the teachings of Yim with the teachings of Chan, Zhang, Hu, and Jiang in order to obtain the invention as claimed in Claim 4.
As to Claim 5, Chan in view Zhang, Hu and Jiang fails to teach by the plurality of monitor devices, accessing a plurality of camera parameter information of a plurality of cameras of the plurality of monitor devices as the plurality of image capturing data. However, Yim teaches that camera parameter information can be obtained through the monitor device (see paragraph [0030], “Through an image calibration process of the camera, an intrinsic parameter and an extrinsic parameter may be obtained. The intrinsic parameter refers to internal information of the camera, such as structural errors of the camera caused by lens distortion or the like. The extrinsic parameter refers to information on how much the actually installed camera is moved from the origin of the world coordinate system and how much the camera is rotated”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Yim with the teachings of Chan, Jiang, Hu and Zhang. The motivation for doing so would be to better estimate the positions objects within images by using camera parameters. Yim teaches in paragraph [0024], “ When camera parameters are given, absolute coordinate information corresponding to an absolute distance may be obtained.” Thus, it would have been obvious to combine the teachings of Yim with the teachings of Chan, Zhang, Hu and Jiang in order to obtain the invention as claimed in Claim 5.
As to Claim 12, Claim 12 claims the same limitation claimed as Claim 3 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 3.
As to Claim 13, Claim 13 claims the same limitation claimed as Claim 4 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 4.
As to Claim 14, Chan in view of Zhang and Hu teaches a plurality of monitor devices, wherein each monitor device comprises:
a camera, configured to capture a corresponding one of the plurality of images; (see Chan, paragraph [0026], “a plurality of cameras arranged to obtain a plurality of images representing the objects to be counted in a target area”).
Chan fails to teach that each monitor comprises a storage configured to store camera parameter information of the camera and a processor, coupled to the camera and the storage, and configured to access the camera parameter information as a corresponding one of the plurality of image capturing data.
However, Jiang teaches a computing system (see Fig.6, computing system 600) , which includes a processor and storage. Jiang further teaches that this computing system can be integrated into a camera (see paragraph [0061] “For example, the system 600 may comprise, or be comprised in, an apparatus, such as a mobile phone, smart phone, camera (e.g., OzO, closed circuit television, webcam), drone, self-driving vehicle, car, unmanned aerial vehicle, autonomous vehicle, and/or Internet of Things (IoT sensor, such as a traffic sensor, industrial sensor, and/or the like) to enable counting of objects, in accordance with some example embodiments”).
Jiang fails to teach that the processor and storage are configured to store camera parameter information and access camera parameter information.
However, Yim teaches that a processing unit (see Fig. 1, ground coordinate conversion unit 133) that uses stored camera parameters in order to calculate the positions of objects in images (see paragraph [0028], “The ground coordinate conversion unit 133 converts the pair of first position coordinates into a pair of second position coordinates of a ground coordinate system using the camera parameters”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Yim with the teachings of Chan, Zhang, Hu, and Jiang. The motivation for doing so would be to better estimate the positions objects within images by using camera parameters. Yim teaches in paragraph [0024], “ When camera parameters are given, absolute coordinate information corresponding to an absolute distance may be obtained.” Thus, it would have been obvious to combine the teachings of Yim with the teachings of Chan, Zhang, Hu, and Jiang in order to obtain the invention as claimed in Claim 14.
As to Claim 15, Chan in view of Zhang, Hu and Jiang teaches camera parameter information comprises camera intrinsic and camera extrinsic coefficients (see Chan, paragraph [0119], “The cameras' intrinsic and extrinsic parameters are estimated using a calibration algorithm”.
Chan in view of Jiang, Hu, and Zhang fails to explicitly teach that distortion coefficients are included within the camera parameters. However, Yim teaches distortion parameters can be obtained (see paragraph [0031] “Through an image calibration process of the camera, an intrinsic parameter and an extrinsic parameter may be obtained. The intrinsic parameter refers to internal information of the camera, such as structural errors of the camera caused by lens distortion or the like. The extrinsic parameter refers to information on how much the actually installed camera is moved from the origin of the world coordinate system and how much the camera is rotated”
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Yim with the teachings of Chan, Zhang, Hu and Jiang. The motivation for doing so would be to correct for distortion to obtain more accurate measurements of distances between objects in images. Yim teaches in paragraph [0041], “ The direct camera-to-person distance calculation unit 170 detects heads of persons in a circle having a predetermined size based on a center of a screen in the image input from a wide angle camera 730 that is installed to face downward and converts the number of pixels between the detected heads of the persons into a distance to calculate the person-to-person distance…A radius of the circle may vary according to a distortion rate of a lens of the camera and software of the camera. The number of pixels between the central points of the head regions is proportional to the actual distance. Therefore, the distance may be calculated by multiplying the distance by the ratio value that varies according to the installation height of the camera.” Thus, it would have been obvious to combine the teachings of Yim with the teachings of Chan, Zhang, Hu, and Jiang in order to obtain the invention as claimed in Claim 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Serackis et al. (US Pub No 20230067081) teaches a method for generating a 3D representation of human joints which comprises obtaining 2D imaging data and generating a 2D heat map.
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/S.T./Examiner, Art Unit 2664
/CHARLOTTE M BAKER/Primary Examiner, Art Unit 2664
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