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.