GAIT analysis: 3D pose estimation and prediction in defence applications using pattern recognition

Mohammad Farukh Hashmi; B. Kiran Kumar Ashish; Avinash G. Keskar

doi:10.1117/12.2559368

31 January 2020 GAIT analysis: 3D pose estimation and prediction in defence applications using pattern recognition

Mohammad Farukh Hashmi, B. Kiran Kumar Ashish, Avinash G. Keskar

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330S (2020) https://doi.org/10.1117/12.2559368
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Availability of humungous visual data and increasing in generation of visual data in Security and Surveillance domain made a pathway to Computer Vision algorithms. The existing algorithms are not precise enough for predictive analytics. Sensitive use cases such as action recognition and identifying missing people in huge crowds has thrown a challenging research of drawing accurate and precise results. The existing 2-D plots for action recognition have failed due to unstructured visual data available where the accuracy is around <50%. Due to unstructured visual data, the existing 3-D plots often get overlapped with each other. Although the accuracy is noted >90% which maps it to False Positives. The existing solutions deals with object detection through Boolean logic then Pose Plots are mapped. Our research focus in on reverse engineer the existing solutions by applying smart segmentation to isolate background and then map the pose formula to detect the action. Our proposed solution obliterates the over-lap complications and unravels the False Positives. Our proposed solution achieved accuracy and precision of mAP>0.8 for both images and video feeds.

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Mohammad Farukh Hashmi, B. Kiran Kumar Ashish, and Avinash G. Keskar "GAIT analysis: 3D pose estimation and prediction in defence applications using pattern recognition", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330S (31 January 2020); https://doi.org/10.1117/12.2559368

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KEYWORDS

Video

Image segmentation

Defense and security

Pattern recognition

Visualization

3D vision

Detection and tracking algorithms

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