False positive elimination in object detection methods for videos
Shubham Dubey, J. V. Satyanarayana, C. Krishna Mohan
Abstract
A robust object detection algorithm is essential while detecting objects in videos and real time scenarios, where false positives might result in unwanted outcomes. Our goal here is to observe how Simple Online and Real-time Tracking with a Deep association metric (Deep SORT) algorithm for Multi-Object Tracking (MOT) can be used to minimize false positives, from a state of the art detection algorithm like You Only Look Once (YOLO), by using the Kalman filter approach. An auto encoder based feature extractor has been used, instead of the standard CNN networks like ResNet-50 to further improve speed of the detector. There have been other MOT algorithms in the recent times which give good results, but are not as real time efficient as the simple yet efficient Deep SORT method. Experimental analysis has shown how Autoencoder based Deep SORT performs in contrast to native Deep SORT and YOLO, in eliminating false positive detections.