收缩骨骼运动学用于人类相关视频异常检测
Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
AI总结：
提出COSKAD模型，利用图卷积网络编码骨骼运动并收缩嵌入到最小体积潜在空间，在多个人类相关视频异常检测基准上达到最先进性能。
摘要：
检测人类行为的异常对于及时识别危险情况（如街头斗殴或老人跌倒）至关重要。然而，异常检测是复杂的，因为异常事件罕见，且这是一个开放集识别任务，即推理时的异常在训练时未被观察到。我们提出了COSKAD，一种新颖的模型，它通过图卷积网络编码骨骼人体运动，并学习将骨骼运动学嵌入收缩到最小体积的潜在超球面上，用于视频异常检测。我们提出了三种潜在空间：常用的欧几里得空间以及新颖的球面和双曲空间。所有变体在最新的UBnormal数据集上均优于现有最先进方法，为此我们贡献了一个带有标注骨骼的人类相关版本。COSKAD在ShanghaiTech Campus和CUHK Avenue的人类相关版本上设立了新的最先进性能，与基于视频的方法性能相当。源代码和数据集将在接受后发布。
英文摘要：
Detecting the anomaly of human behavior is paramount to timely recognizing endangering situations, such as street fights or elderly falls. However, anomaly detection is complex since anomalous events are rare and because it is an open set recognition task, i.e., what is anomalous at inference has not been observed at training. We propose COSKAD, a novel model that encodes skeletal human motion by a graph convolutional network and learns to COntract SKeletal kinematic embeddings onto a latent hypersphere of minimum volume for Video Anomaly Detection. We propose three latent spaces: the commonly-adopted Euclidean and the novel spherical and hyperbolic. All variants outperform the state-of-the-art on the most recent UBnormal dataset, for which we contribute a human-related version with annotated skeletons. COSKAD sets a new state-of-the-art on the human-related versions of ShanghaiTech Campus and CUHK Avenue, with performance comparable to video-based methods. Source code and dataset will be released upon acceptance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
