论文标题
视频中战斗的检测：对异常检测和动作识别的比较研究
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition
论文作者
论文摘要
战斗的检测是视频中重要的监视应用。大多数现有方法都使用监督的二进制动作识别。由于框架级注释很难获得异常检测，因此使用多个实例学习的弱监督学习被广泛使用。本文探讨了视频中的战斗作为一种特殊类型的异常检测和二进制动作识别。我们在大多数研究中都使用UBI-Fight和NTU-CCTV-Fight数据集，因为它们具有框架级注释。我们发现，异常检测的性能甚至比动作识别更好。此外，我们研究使用异常检测作为工具箱，以迭代方式生成训练数据集，以迭代方式识别以异常检测的性能为条件。实验结果应表明，我们在三个战斗检测数据集上实现了最先进的性能。
Detection of fights is an important surveillance application in videos. Most existing methods use supervised binary action recognition. Since frame-level annotations are very hard to get for anomaly detection, weakly supervised learning using multiple instance learning is widely used. This paper explores the detection of fights in videos as one special type of anomaly detection and as binary action recognition. We use the UBI-Fight and NTU-CCTV-Fight datasets for most of the study since they have frame-level annotations. We find that the anomaly detection has similar or even better performance than the action recognition. Furthermore, we study to use anomaly detection as a toolbox to generate training datasets for action recognition in an iterative way conditioned on the performance of the anomaly detection. Experiment results should show that we achieve state-of-the-art performance on three fight detection datasets.
