Detecting Violence in Video using Subclasses

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最新推荐文章于 2025-02-10 06:58:52 发布

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本文提出一种新的视频暴力行为检测方法，通过引入与暴力视觉相关的子类别来增强多模态特征融合的效果。该方法不仅提高了对暴力行为理解的精细度，还在MediaEval 2015数据集上取得了显著的效果提升。

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Detecting Violence in Video using Subclasses

Xirong Li, Yujia Huo, Jieping Xu, Qin Jin

(Submitted on 27 Apr 2016)

This paper attacks the challenging problem of violence detection in videos. Different from existing works focusing on combining multi-modal features, we go one step further by adding and exploiting subclasses visually related to violence. We enrich the MediaEval 2015 violence dataset by \emph{manually} labeling violence videos with respect to the subclasses. Such fine-grained annotations not only help understand what have impeded previous efforts on learning to fuse the multi-modal features, but also enhance the generalization ability of the learned fusion to novel test data. The new subclass based solution, with AP of 0.303 and P100 of 0.55 on the MediaEval 2015 test set, outperforms several state-of-the-art alternatives. Notice that our solution does not require fine-grained annotations on the test set, so it can be directly applied on novel and fully unlabeled videos. Interestingly, our study shows that motion related features, though being essential part in previous systems, are dispensable.

Subjects:	Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:1604.08088 [cs.MM]
	(or arXiv:1604.08088v1 [cs.MM] for this version)