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MA-CBP: A Criminal Behavior Prediction Framework Based on Multi-Agent Asynchronous Collaboration

MA-CBP: A Criminal Behavior Prediction Framework Based on Multi-Agent Asynchronous Collaboration

来源:Arxiv_logoArxiv
英文摘要

With the acceleration of urbanization, criminal behavior in public scenes poses an increasingly serious threat to social security. Traditional anomaly detection methods based on feature recognition struggle to capture high-level behavioral semantics from historical information, while generative approaches based on Large Language Models (LLMs) often fail to meet real-time requirements. To address these challenges, we propose MA-CBP, a criminal behavior prediction framework based on multi-agent asynchronous collaboration. This framework transforms real-time video streams into frame-level semantic descriptions, constructs causally consistent historical summaries, and fuses adjacent image frames to perform joint reasoning over long- and short-term contexts. The resulting behavioral decisions include key elements such as event subjects, locations, and causes, enabling early warning of potential criminal activity. In addition, we construct a high-quality criminal behavior dataset that provides multi-scale language supervision, including frame-level, summary-level, and event-level semantic annotations. Experimental results demonstrate that our method achieves superior performance on multiple datasets and offers a promising solution for risk warning in urban public safety scenarios.

Weichao Wu、Cheng Liu、Daou Zhang、Tingxu Liu、Yuhan Wang、Jinyang Chen、Yuexuan Li、Xinying Xiao、Chenbo Xin、Ziru Wang

安全科学计算技术、计算机技术

Weichao Wu,Cheng Liu,Daou Zhang,Tingxu Liu,Yuhan Wang,Jinyang Chen,Yuexuan Li,Xinying Xiao,Chenbo Xin,Ziru Wang.MA-CBP: A Criminal Behavior Prediction Framework Based on Multi-Agent Asynchronous Collaboration[EB/OL].(2025-08-19)[2025-08-24].https://arxiv.org/abs/2508.06189.点此复制

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