Adaptive-Modeling Multi-Agent Learning System for Video Behavioral Clustering Recognition
Source abstract
Abstract
Multi-agent systems are suitable for handling complex problems due to their high parallelism and autonomous evolution ability. In this paper, we propose an adaptive clustering multi-agent learning system for intelligent applications with continuously changing requirements. Each agent model changes temporal sequences using the longest common subsequence (LCS) algorithm. Multiple agents collaborate in a multilayer decentralized approach to enhance learning adaptability and achieve self-supervised behavioral clustering. The system is constructed using a “memory-like” method and operates primarily on memory access and comparison, avoiding extensive matrix operations of artificial neural networks while achieving learning and prediction functions. We chose an unsupervised vehicle behavioral clustering scenario for feasibility validation in which the system’s cognitive objective is to cluster and recognize vehicle behaviors. In a low computational environment, the system can complete clustering functions and exhibit continuous learning capabilities when new behavioral changes occur. The proposed approach achieves an accuracy of 97.4% while processing at a speed 1–5 times faster than similar clustering algorithms. The verification results indicate that this system has excellent potential to enhance intelligent sensing front ends.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Anomaly Detection Techniques and Applications · Data Stream Mining Techniques · Human Pose and Action Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-System and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
School of Physical Science and Technology, Shanghaitech University, Shanghai 201210, China
NeuHelium Co., Ltd., Shanghai 200050, China
Funding
Strategic Priority Research Program of the Chinese Academy of Sciences
References (23)
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