Title: Robust autonomous detection and tracking of moving objects using hybrid tracking approach

Authors: Mohamed Akli Bousta; Abdelkrim Nemra

Addresses: École Militaire Polytechnique, BP17, Bordj El Bahri, Alger, Algeria ' École Militaire Polytechnique, BP17, Bordj El Bahri, Alger, Algeria

Abstract: Detecting and tracking mobile objects in video is among the most prevalent and challenging tasks under realistic motion and climatic conditions such as image occlusion, fast camera movement and natural environmental changes (fog, rain, etc.). In this paper, we propose an improved autonomous visual detection and tracking algorithm, which uses the single shot detection algorithm for initialisation followed by an adaptive kernelised correlation filter (KCF) tracker and combined with a predictor-corrector smooth variable structure filter (SVSF) for target recovery and estimation. It is known that KCF tracker suffers from failure to target recovery after an occlusion and scale variation. To overcome these limitations, the optimal SVSF filter is combined with the KCF tracker in order to maintain suitable target estimation and update the KCF tracker when the target is lost. The obtained results illustrate that the proposed approach achieves the state-of-the-art performance on all tested datasets with many realistic scenarios with different attributes.

Keywords: visual detection and tracking; single shot multi-box detector; SSD; kernelised correlation filter; KCF; smooth variable structure filter; SVSF.

DOI: 10.1504/IJCVR.2024.139545

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.4, pp.375 - 400

Received: 18 Dec 2021
Accepted: 25 Jul 2022
Published online: 04 Jul 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article