Preprint Communication Version 1 Preserved in Portico This version is not peer-reviewed

Adaptive Modeling Multi-Agent Learning System for Video Behavior Clustering Recognition

Version 1 : Received: 8 May 2023 / Approved: 9 May 2023 / Online: 9 May 2023 (08:23:29 CEST)

How to cite: Qian, X.; Yuemaier, A.; Yang, W.; Chen, X.; Li, S.; Dai, W.; Song, Z. Adaptive Modeling Multi-Agent Learning System for Video Behavior Clustering Recognition. Preprints 2023, 2023050600. https://doi.org/10.20944/preprints202305.0600.v1 Qian, X.; Yuemaier, A.; Yang, W.; Chen, X.; Li, S.; Dai, W.; Song, Z. Adaptive Modeling Multi-Agent Learning System for Video Behavior Clustering Recognition. Preprints 2023, 2023050600. https://doi.org/10.20944/preprints202305.0600.v1

Abstract

In this work, we propose a self-supervised multi-agent system that meets the online learning of clustering tasks for video behavior recognition spatio-temporal tasks. Encoding visual behavioral actions as discrete temporal sequence(DTS). Real-time clustering recognition task in a multi-agent system for continuous model building, training, and correction. Finally, we implemented a fully decentralized multi-agent system and completed its feasibility verification in a surveillance video application scenario on vehicle path clustering.

Keywords

Continuous Learning; Multi-agent System; Prediction; Adaptability

Subject

Computer Science and Mathematics, Computer Vision and Graphics

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