论文标题
聚类有助于弱监督培训，以检测监视视频中的异常事件
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos
论文作者
论文摘要
为仅使用视频级标签检测现实世界异常事件的学习系统是一项具有挑战性的任务，这主要是由于存在嘈杂的标签以及训练数据中罕见发生异常事件的情况。我们提出了一个弱监督的异常检测系统，该系统具有多种贡献，包括一种随机的批次选择机制，以减少批处理间相关性和正常抑制块，该抑制块通过利用训练批处理中可用的整体信息来最大程度地减少视频正常区域的异常评分。此外，提出了一个聚类损失块，以减轻标签噪声并改善异常和正常区域的表示形式学习。该块鼓励骨干网络产生两个不同的特征簇，代表正常事件和异常事件。使用三个流行的异常检测数据集（包括UCF-Crime，Shanghaitech和UCSD PED2）提供了对拟议方法的广泛分析。实验证明了我们方法的出色异常检测能力。
Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well as the rare occurrence of anomalous events in the training data. We propose a weakly supervised anomaly detection system which has multiple contributions including a random batch selection mechanism to reduce inter-batch correlation and a normalcy suppression block which learns to minimize anomaly scores over normal regions of a video by utilizing the overall information available in a training batch. In addition, a clustering loss block is proposed to mitigate the label noise and to improve the representation learning for the anomalous and normal regions. This block encourages the backbone network to produce two distinct feature clusters representing normal and anomalous events. Extensive analysis of the proposed approach is provided using three popular anomaly detection datasets including UCF-Crime, ShanghaiTech, and UCSD Ped2. The experiments demonstrate a superior anomaly detection capability of our approach.
