Sequential Keypoint Density Estimator: An Overlooked Baseline of Skeleton-Based Video Anomaly Detection

Abstract

Detecting anomalous human behaviouris an important visual taskin safety-critical applicationssuch as healthcare monitoring,workplace safety,or public surveillance.In these contexts,abnormalities are often reflectedwith unusual human poses.Thus, we propose SeeKer,a method for detecting anomaliesin sequences of human skeletons.Our method formulates the skeleton sequence densitythrough autoregressive factorization at the keypoint level.The corresponding conditional distributionsrepresent probable keypoint locations given prior skeletal motion.We formulate the joint distribution of the considered skeletonas causal prediction of conditional Gaussiansacross its constituent keypoints.A skeleton is flagged as anomalous if its keypoint locations surprise our model(i.e. receive a low density).In practice, our anomaly score is a weighted sum of per-keypoint log-conditionals,where the weights account for the confidence of the underlying keypoint detector.Despite its conceptual simplicity,SeeKer surpasses all previous methodson the UBnormal and MSAD-HR datasetswhile delivering competitive performanceon the ShanghaiTech dataset.