Assessing the impact of feature communication in swarm perception for people re-identification

Miquel Kegeleirs¹Ilyes Gharbi²Lorenzo Garattoni³Gianpiero Francesca³Mauro Birattari^2*

• ¹Université libre de Bruxelles, Brussels, Belgium
• ²Universite Libre de Bruxelles, Brussels, Belgium
• ³Toyota Motor Europe NV SA, Zaventem, Belgium

Swarm perception enables a robot swarm to collectively sense and interpret the environment by integrating sensory inputs from individual robots. In this study, we explore its application to people re-identification, a critical task in multi-camera tracking scenarios. We propose a decentralized, feature-based perception method that allows robots to re-identify people across different viewpoints. Our approach combines detection, tracking, re-identification, and clustering algorithms, enhanced by a model trained to refine extracted features. Robots dynamically share and fuse data in a decentralized manner, ensuring that collected information remains up to date. Simulation results, measured by the cumulative matching characteristics (CMC) curve, mean average precision (mAP), and average cluster purity, show that decentralized communication significantly improves performance, enabling robots to outperform static cameras without communication and, in some cases, even centralized communication. Furthermore, the findings suggest a trade-off between the amount of data shared and the consistency of the Re-ID.