Enhanced Person Tracking with Metric-Based Learning and Diverse Detector Integration (DDI)

In this paper, we introduce a person-tracking method that utilizes a metric-based learning approach for object association during tracking. We evaluated the performance of this person association algorithm using various state-of-the-art detectors like Yolo V4, Resent, Faster RCNN, etc. based on the MOT dataset. Our main objective was to improve the accuracy and precision of multiple object tracking and to reduce ID switching, which are some of the problems related to various object tracking methods. Our approach leverages powerful detectors to effectively handle a large number of detected objects. The use of accurate feature extraction provides the metric learning methods with the opportunity to make correct associations with the right identity. This method is also capable of solving the person re-identification problem.