Motion Based Event Analysis

Abstract

Motion is an important cue in videos that captures the dynamics of moving objects. It helps in effective analysis of various event related tasks such as human action recognition, anomaly detection, tracking, crowd behavior analysis, traffic monitoring, etc. Generally, accurate motion information is computed using various optical flow estimation techniques. On the other hand, coarse motion information is readily available in the form of motion vectors in compressed videos. Utilizing these encoded motion vectors reduces the computational burden involved in flow estimation and enables rapid analysis of video streams. In this work, the focus is on analyzing motion patterns, retrieved from either motion vectors or optical flow, in order to do various event analysis tasks such as video classification, anomaly detection and crowd flow segmentation. In the first section, we utilize the motion vectors from H.264 compressed videos, a compression standard widely used due to its high compression ratio, to address the following problems. i) Video classification: This work proposes an approach to classify videos based on human action by capturing spatio-temporal motion pattern of the actions using Histogram of Oriented Motion Vector (HOMV) ii) Crowd flow segmentation: In this work, we have addressed the problem of flow segmentation of the dominant motion patterns of the crowds. The proposed approach combines multi-scale super-pixel segmentation of the motion vectors to obtain the final flow segmentation. iii) Anomaly detection: This problem is addressed by local modeling of usual behavior by capturing features such as magnitude and orientation of each moving object. In all the above approaches, the focus was to reduce computations while retaining comparable accuracy to pixel domain processing. In second section, we propose two approaches for anomaly detection using optical flow. The first approach uses spatio-temporal low level motion features and detects anomalies based on the reconstruction error of the sparse representation of the candidate feature over a dictionary of usual behavior features. The main contribution is in enhancing each local dictionary by applying an appropriate transformation on dictionaries of the neighboring regions. The other algorithm aims to improve the accuracy of anomaly localization through short local trajectories of super pixels belonging to moving objects. These trajectories capture both spatial as well as temporal information effectively. In contrast to compressed domain analysis, these pixel level approaches focus on improving the accuracy of detection with reasonable detection speed.