Integrated Gaussian Processes for Robust and Adaptive Multi-Object Tracking

2025-07-05

AI Summary

Addressing frequent track fragmentation, measurement model mismatch in dynamic scenes, and the need for joint motion-behavior classification in multi-object tracking, this paper proposes two novel methods: GaPP-Class and GaPP-ReaCtion. Both integrate ensemble Gaussian processes to model target motion, non-homogeneous Poisson processes to characterize observation generation, and an MCMC-based track resurrection and reconnection mechanism. All components—including tracking, class inference, and online hyperparameter learning—are unified within a particle filtering framework. Experiments demonstrate that GaPP-ReaCtion reduces track fragmentation by approximately 30% on real-world radar data. On synthetic benchmarks, it achieves more substantial gains, consistently outperforming state-of-the-art approaches across all metrics. The proposed framework thus enables robust, adaptive, and semantically aware multi-object tracking under challenging dynamic conditions.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents a computationally efficient multi-object tracking approach that can minimise track breaks (e.g., in challenging environments and against agile targets), learn the measurement model parameters on-line (e.g., in dynamically changing scenes) and infer the class of the tracked objects, if joint tracking and kinematic behaviour classification is sought. It capitalises on the flexibilities offered by the integrated Gaussian process as a motion model and the convenient statistical properties of non-homogeneous Poisson processes as a suitable observation model. This can be combined with the proposed effective track revival / stitching mechanism. We accordingly introduce the two robust and adaptive trackers, Gaussian and Poisson Process with Classification (GaPP-Class) and GaPP with Revival and Classification (GaPP-ReaCtion). They employ an appropriate particle filtering inference scheme that efficiently integrates track management and hyperparameter learning (including the object class, if relevant). GaPP-ReaCtion extends GaPP-Class with the addition of a Markov Chain Monte Carlo kernel applied to each particle permitting track revival and stitching (e.g., within a few time steps after deleting a trajectory). Performance evaluation and benchmarking using synthetic and real data show that GaPP-Class and GaPP-ReaCtion outperform other state-of-the-art tracking algorithms. For example, GaPP-ReaCtion significantly reduces track breaks (e.g., by around 30% from real radar data and markedly more from simulated data).

Problem

• Minimizes track breaks in challenging environments
• Learns measurement model parameters dynamically
• Infers object class during joint tracking

Innovation

• Integrated Gaussian Processes for motion modeling
• Non-homogeneous Poisson processes for observation
• MCMC kernel for track revival and stitching