Problem
Research questions and friction points this paper is trying to address.
multi-object tracking
data association
stochastic methods
deterministic methods
tracking robustness
GenTrack3: Hybrid Stochastic-Deterministic Online Multi-Object Tracking with Cluster-Aware Association
🤖 AI Summary
This work addresses the challenges of association errors and insufficient robustness in multi-object tracking caused by noisy detections and complex dynamics. To this end, the authors propose a hybrid stochastic-deterministic online tracking framework that introduces a trajectory meta-model—comprising identity, state, velocity, penalty, and lifespan—and leverages particle sets to represent uncertainty. A cluster-aware trajectory-to-detection matching mechanism is designed to enable efficient data association. The proposed approach significantly enhances tracking robustness, accuracy, and scalability in dense and complex scenes while maintaining computational efficiency, and it effectively supports group target tracking.
📝 Abstract
Multi-object tracking (MOT) involves maintaining consistent target identities as objects dynamically enter and leave a scene. Deterministic approaches, such as tracking-by-detection with data association, produce reproducible results and are computationally efficient, but they rely heavily on motion models and are sensitive to noisy detections that can lead to association errors. In contrast, stochastic methods explicitly model uncertainty and can better handle complex non-linear dynamics, albeit at the cost of increased computational complexity and variability arising from random sampling. This paper presents an online MOT framework that integrates deterministic and stochastic principles to achieve robust tracking under uncertainty. Furthermore, a novel track-to-detection matching approach is introduced to enhance scalability with increasing target numbers while supporting group tracking. The tracking inference mechanism employs a tracklet that includes identifiers, states, velocities, track penalties and track ages of targets, supporting a systematic tracking pipeline. Each target is associated with a stochastic particle set to compute the matching cost to detections. Reference implementations of the proposed approach and baseline trackers can be found on GitHub: https://github.com/SDU-VelKoTek/GenTrack3.
Innovation
Methods, ideas, or system contributions that make the work stand out.
hybrid stochastic-deterministic tracking
cluster-aware association
online multi-object tracking
particle-based matching cost
tracklet representation
🔎 Similar Papers
No similar papers found.
