Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets

Abstract

This paper proposes an online visual multi-object tracking (MOT) algorithm that resolves object appearance-reappearance and occlusion. Our solution is based on the labeled random finite set (LRFS) filtering approach, which in principle, addresses disappearance, appearance, reappearance, and occlusion via a single Bayesian recursion. However, in practice, existing numerical approximations cause reappearing objects to be initialized as new tracks, especially after long periods of being undetected. In occlusion handling, the filter's efficacy is dictated by trade-offs between the sophistication of the occlusion model and computational demand. Our contribution is a novel modeling method that exploits object features to address reappearing objects whilst maintaining a linear complexity in the number of detections. Moreover, to improve the filter's occlusion handling, we propose a fuzzy detection model that takes into consideration the overlapping areas between tracks and their sizes. We also develop a fast version of the filter to further reduce the computational time. The source code is publicly available at https://github.com/linh-gist/mv-glmb-ab.

References in corpus (6)

• FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
• HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
• Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter
• A Bayesian Filter for Multi-view 3D Multi-object Tracking with Occlusion Handling
• Real-Time Siamese Multiple Object Tracker with Enhanced Proposals
• Kinematics Modeling Network for Video-based Human Pose Estimation

Cited by in corpus (2)

• Tracking by Detection and Query: An Efficient End-to-End Framework for Multi-Object Tracking
• SWIR-LightFusion: Multi-spectral Semantic Fusion of Synthetic SWIR with Thermal IR (LWIR/MWIR) and RGB