Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications

Abstract

Multi-object detection and tracking systems represent one of the basic and important tasks of surveillance and video traffic systems. Recently. The proposed tracking algorithms focused on the detection mechanism. It showed significant improvement in performance in the field of computer vision. Though. It faced many challenges and problems, such as many blockages and segmentation of paths, in addition to the increasing number of identification keys and false-positive paths. In this work, an algorithm was proposed that integrates information on appearance and visibility features to improve the tracker's performance. It enables us to track multiple objects throughout the video and for a longer period of clogging and buffer a number of ID switches. An effective and accurate data set, tools, and metrics were also used to measure the efficiency of the proposed algorithm. The experimental results show the great improvement in the performance of the tracker, with high accuracy of more than 65%, which achieves competitive performance with the existing algorithms.

Improve Multi-Object Detection and Tracking for an Automated Traffic Surveillance System

“…Researchers in [17] extract valuable information with high speed and accuracy through an efficient approach for detecting and tracking multiple objects, calculating critical detection, and tracking performance characteristics such as training and learning efficiency, as well as the influence of training sample sizes, inspecting some problems to assist the algorithm, giving good results and stable performance training. Also, several Deep Learning Networks (DLN) detection models were tested on the NVidia Jetson (TX2) Platform, and the suggested approach was also implemented in [17]. The suggested method in [18] employed an effective Kalman filtering algorithm to track moving objects in video frames.…”