Motion Pattern Study and Analysis from Video Monitoring Trajectory

Abstract

SUMMARYThis paper introduces an unsupervised method for motion pattern learning and abnormality detection from video surveillance. In the preprocessing steps, trajectories are segmented based on their locations, and the sub-trajectories are represented as codebooks. Under our framework, Hidden Markov Models (HMMs) are used to characterize the motion pattern feature of the trajectory groups. The state of trajectory is represented by a HMM and has a probability distribution over the possible output sub-trajectories. Bayesian Information Criterion (BIC) is introduced to measure the similarity between groups. Based on the pairwise similarity scores, an affinity matrix is constructed which indicates the distance between different trajectory groups. An Adaptable Dynamic Hierarchical Clustering (ADHC) tree is proposed to gradually merge the most similar groups and form the trajectory motion patterns, which implements a simpler and more tractable dynamical clustering procedure in updating the clustering results with lower time complexity and avoids the traditional overfitting problem. By using the HMM models generated for the obtained trajectory motion patterns, we may recognize motion patterns and detect anomalies by computing the likelihood of the given trajectory, where a maximum likelihood for HMM indicates a pattern, and a small one below a threshold suggests an anomaly. Experiments are performed on EIFPD trajectory datasets from a structureless scene, where pedestrians choose their walking paths randomly. The experimental results show that our method can accurately learn motion patterns and detect anomalies with better performance.

A review of abnormal behavior research based on motion characteristics

“…Literature [21] Based on target tracking and trajectory analysis methods, a sparse anomaly detection model is established to realize multi-target joint anomaly detection. Literature [22] The hidden Markov model is generated by using trajectory motion pattern features to detect abnormal behavior in the video. The recognition effect of trajectory analysis of human behavior largely depends on the accuracy of target tracking.…”

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

“…Based on human trajectory clustering, trajectory clusters are obtained, and behavior patterns of specific people are mined. Kang et al [37] used dynamic hierarchical clustering to describe a certain type of action pattern by using the central trajectory, and measured the similarity between the detection trajectory and the central trajectory to determine whether the anomaly was found. Lee et al [38] proposed a trajectory segmentation detection framework and the trajectory clustering algorithm TRACLUS, which introduces the standard MDL (Minimum Description Length) widely adopted in information compression to extract the velocity feature points of the trajectory.…”