Identification and Tracking of Groups of People using Object Detection and Object Tracking

Object detection is one of the most important areas in the fields of Data Science and Computer Vision. In this paper, we present a novel approach to identifying and tracking groups of people, couples, and individuals in videos by using deep learning-based object detection and object tracking techniques along with a proposed grouping algorithm. In this approach, transfer learning is applied on YOLO v3 model for the detection of people in video frames, and Deep SORT is applied for tracking each detected person throughout the video. Results obtained from person detection and person tracking were used by the proposed grouping algorithm to identify and track groups, couples, and individuals who are appearing in input videos. Our proposed grouping algorithm is based on the proximity between each individual and the time duration that proximity is maintained for. It also considers how to identify and track groups, when people are moving within the groups. This approach was evaluated using CCTV videos captured from the restaurant domain and it was able to perform the group detection and tracking tasks successfully with a precision of 0.7083, recall of 0.7906 and F1 score of 0.7471.