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General Research Article

The Impact of the Detector on the Performances of a Multi-Person Tracking System

Author(s): Djalal Djarah*, Abdallah Meraoumia and Mohamed Lakhdar Louazene

Volume 16, Issue 2, 2022

Published on: 15 February, 2021

Article ID: e180122191413 Pages: 9

DOI: 10.2174/1872212115666210215125959

Abstract

Background: Pedestrian detection and tracking are an important area of study in realworld applications, such as mobile robots, human-computer interaction, video surveillance, pedestrian protection systems, etc. As a result, it has attracted the interest of the scientific community.

Objective: Certainly, tracking people is critical for numerous utility areas which cover unusual situations detection, like vicinity evaluation, and sometimes change direction in human gait and partial occlusions.

Researchers' primary focus is to develop a surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. So, it has become a significant issue and challenge to design a tracking system that can be more suitable for such situations. To this end, this paper presents a comparative evaluation of the tracking-by-detection system along with the publicly available pedestrian benchmark databases.

Method: Unlike recent works where person detection and tracking are usually treated separately, our work explores the joint use of the popular Simple Online and Real-time Tracking (SORT) method and the relevant visual detectors. Consequently, the choice of the detector is an important factor in the evaluation of the system's performance.

Results: Experimental results demonstrate that the performance of the tracking-by-detection system is closely related to the optimal selection of the detector and should be required prior to a rigorous evaluation.

Conclusion: The study demonstrates how sensitive the system performance as a whole is to the challenges of the dataset. Furthermore, the efficiency of the detector and the detector-tracker combination is also depending on the dataset.

Keywords: Tracking-by-detection, detection, multi-person tracking, data association.

Graphical Abstract

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