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From Explicit Rules to Implicit Reasoning in an Interpretable Violence Monitoring System

Recently, research based on pre-trained models has demonstrated outstanding performance in violence surveillance tasks. However, most of them were black-box systems which faced challenges regarding explainability during training and inference processes. An important question is how to incorporate explicit knowledge into these implicit models, thereby designing expertdriven and interpretable violence surveillance systems. This paper proposes a new paradigm for weakly supervised violence monitoring (WSVM) called Rule base Violence Monitoring (RuleVM). The proposed RuleVM uses a dual-branch structure with different designs for images and text. One of the branches is called the implicit branch, which uses only visual features for coarse-grained binary classification. In this branch, image feature extraction is divided into two channels: one responsible for extracting scene frames and the other focusing on extracting actions. The other branch is called the explicit branch, which utilizes language-image alignment to perform fine-grained classification. For the language channel design in the explicit branch, the proposed RuleVM uses the state-of-the-art YOLOWorld model to detect objects in video frames, and association rules are identified through data mining methods as descriptions of the video. Leveraging the dual-branch architecture, RuleVM achieves interpretable coarse-grained and fine-grained violence surveillance. Extensive experiments were conducted on two commonly used benchmarks, and the results show that RuleVM achieved the best performance in both coarse-grained and finegrained monitoring, significantly outperforming existing state-ofthe-art methods. Moreover, interpretability experiments uncovered some interesting rules, such as the observation that as the number of people increases, the risk level of violent behavior also rises.

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