论文标题
监视摄像机基于视觉的战斗检测
Vision-based Fight Detection from Surveillance Cameras
论文作者
论文摘要
基于视觉的动作识别是计算机视觉和模式识别的最具挑战性的研究主题之一。希望在公共区域，监狱等的监视摄像机中检测到它的特定应用，以迅速控制这些暴力事件。本文解决了这一研究问题，并探讨了基于LSTM的方法来解决它。此外，还利用了注意力层。此外，还收集了一个新的数据集，该数据集由YouTube可用的监视摄像头视频中的战斗场景组成。该数据集可公开使用。从曲棍球战斗，Peliculas和新近收集的战斗数据集进行的广泛实验中，可以观察到，拟议的方法集成了Xepperion模型，BI-LSTM和注意力，可以提高战斗场景分类的最新准确性。
Vision-based action recognition is one of the most challenging research topics of computer vision and pattern recognition. A specific application of it, namely, detecting fights from surveillance cameras in public areas, prisons, etc., is desired to quickly get under control these violent incidents. This paper addresses this research problem and explores LSTM-based approaches to solve it. Moreover, the attention layer is also utilized. Besides, a new dataset is collected, which consists of fight scenes from surveillance camera videos available at YouTube. This dataset is made publicly available. From the extensive experiments conducted on Hockey Fight, Peliculas, and the newly collected fight datasets, it is observed that the proposed approach, which integrates Xception model, Bi-LSTM, and attention, improves the state-of-the-art accuracy for fight scene classification.
