GenTrack: A New Generation of Multi-Object Tracking

TL;DR

GenTrack introduces a hybrid stochastic-deterministic multi-object tracking method that leverages particle swarm optimization and social interactions to improve target identity consistency, especially during occlusions and with noisy detections.

Contribution

It presents a novel hybrid tracking approach combining PSO with social interactions, and provides the first publicly available source code for advanced multi-object tracking.

Findings

Outperforms state-of-the-art trackers on standard benchmarks.

Reduces ID switches and track loss during occlusions.

Offers flexible variants and comprehensive baseline for future research.

Abstract

This paper introduces a novel multi-object tracking (MOT) method, dubbed GenTrack, whose main contributions include: a hybrid tracking approach employing both stochastic and deterministic manners to robustly handle unknown and time-varying numbers of targets, particularly in maintaining target identity (ID) consistency and managing nonlinear dynamics, leveraging particle swarm optimization (PSO) with some proposed fitness measures to guide stochastic particles toward their target distribution modes, enabling effective tracking even with weak and noisy object detectors, integration of social interactions among targets to enhance PSO-guided particles as well as improve continuous updates of both strong (matched) and weak (unmatched) tracks, thereby reducing ID switches and track loss, especially during occlusions, a GenTrack-based redefined visual MOT baseline incorporating a comprehensive state and observation model based on space consistency, appearance, detection confidence, track penalties, and social scores for systematic and efficient target updates, and the first-ever publicly available source-code reference implementation with minimal dependencies, featuring three variants, including GenTrack Basic, PSO, and PSO-Social, facilitating flexible reimplementation. Experimental results have shown that GenTrack provides superior performance on standard benchmarks and real-world scenarios compared to state-of-the-art trackers, with integrated implementations of baselines for fair comparison. Potential directions for future work are also discussed. The source-code reference implementations of both the proposed method and compared-trackers are provided on GitHub: https://github.com/SDU-VelKoTek/GenTrack