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