用机器学习提升监控视频暴力事件自动识别能力，更快更准。
Intelligent Image Sensing for Crime Analysis: A ML Approach towards Enhanced Violence Detection and Investigation
- 用3D卷积神经网络+双向LSTM分析视频时序特征。
- 在多源数据集上实现高精度暴力事件分类，支持实时处理。
- 可部署在树莓派上，适合实际安防场景应用。
全球犯罪率持续上升，传统监控手段难以及时发现多样且突发的暴力行为。为应对这一挑战，本文提出一种基于机器学习的自动暴力检测与分类框架，采用监督学习实现二分类和多分类任务。检测模型使用3D卷积神经网络，分类模型则结合可分离3D卷积进行特征提取，搭配双向长短期记忆网络（bidirectional LSTM）处理时间序列信息。训练基于多个自定义数据集，包含监控摄像头、人拍摄视频、冰球打架片段及Sohas、WVD等公开数据集，覆盖多种平台。同时，集成树莓派相机模块实时采集视频流并传输至模型处理，验证了在计算资源效率和准确率上的显著提升。
原文摘要 · Abstract (English)
The increasing global crime rate, coupled with substantial human and property losses, highlights the limitations of traditional surveillance methods in promptly detecting diverse and unexpected acts of violence. Addressing this pressing need for automatic violence detection, we leverage Machine Learning to detect and categorize violent events in video streams. This paper introduces a comprehensive framework for violence detection and classification, employing Supervised Learning for both binary and multi-class violence classification. The detection model relies on 3D Convolutional Neural Networks, while the classification model utilizes the separable convolutional 3D model for feature extraction and bidirectional LSTM for temporal processing. Training is conducted on a diverse customized datasets with frame-level annotations, incorporating videos from surveillance cameras, human recordings, hockey fight, sohas and wvd dataset across various platforms. Additionally, a camera module integrated with raspberry pi is used to capture live video feed, which is sent to the ML model for processing. Thus, demonstrating improved performance in terms of computational resource efficiency and accuracy.
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