Adaptive-Modeling Multi-Agent Learning System for Video Behavioral Clustering Recognition
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Published:2023-06-25
Issue:13
Volume:13
Page:7486
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Qian Xingyu1ORCID, Yuemaier Aximu2ORCID, Yang Wenchi3, Chen Xiaogang1ORCID, Li Shunfen1, Dai Weibang1, Song Zhitang1
Affiliation:
1. State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-System and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China 2. School of Physical Science and Technology, Shanghaitech University, Shanghai 201210, China 3. NeuHelium Co., Ltd., Shanghai 200050, China
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.
Funder
Strategic Priority Research Program of the Chinese Academy of Sciences
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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