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

TL;DR

An online visual multi-object tracking algorithm that resolves object appearance-reappearance and occlusion and a novel modeling method that exploits object features to address reappearing objects whilst maintaining a linear complexity in the number of detections is proposed.

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.

Figures (10)

• Figure 1: A high-level diagram of the proposed LRFS-based MOT algorithm.
• Figure 2: Tracks 1 and 2 overlap each other, but since the bottom corner of track 2 is lower than of track 1, track 2 occludes track 1. Similarly, track 4 occludes track 3.
• Figure 3: Design of our fuzzy detection model capable of handling object occlusion. The core of the model is a set of fuzzy rules and membership functions that represent expert knowledge. The inputs to the model are the maximum IoA score (computed using Algorithm \ref{['alg:maxIoA']}) and the area ratio (computed using (\ref{['eq:Ra']})). The output of the model is the object detection probability.
• Figure 4: Membership functions for different degrees of membership (L, M, H). The range is limited to [0, 2] for $R_{\text{a}}$, to [0, 1] for IoA, and [0.2, 0.99] for detection probability.
• Figure 5: Relationship between variables in the fuzzy model. For illustration, we select 3 points on the plot that represent 3 typical occlusion scenarios: point A is the scenario where the object is covered half (IoA $=0.5$), but it is close to the camera (high area ratio), hence the high detection probability ($>0.9$); point B is the scenario where the object is also covered half, but it is relatively further away from the camera, hence the medium detection probability ($\approx0.5$); and point C is for the scenario where the object is small and far away from the camera, hence the low detection probability ($<0.5$).