Hierarchical activity discovery within spatio-temporal context for video anomaly detection Conference Paper uri icon

abstract

  • In this paper, we present a novel approach for video anomaly detection in crowded and complicated scenes. The proposed approach detects anomalies based on a hierarchical activity pattern discovery framework comprehensively considering both global and local spatio-temporal contexts. The discovery is a coarse-to-fine learning process with unsupervised ways for automatically constructing normal activity patterns at different levels. An unified anomaly energy function is designed based on these discovered activity patterns to identify the abnormal level of an input motion pattern. We demonstrate the efficiency of the proposed method on the UCSD anomaly detection datasets (Ped1 and Ped2) and compare the performance with existing work. © 2013 IEEE.

name of conference

  • 2013 20th IEEE International Conference on Image Processing (ICIP)

published proceedings

  • 2013 IEEE International Conference on Image Processing

author list (cited authors)

  • Xu, D., Wu, X., Song, D., Li, N., & Chen, Y

citation count

  • 22

complete list of authors

  • Xu, Dan||Wu, Xinyu||Song, Dezhen||Li, Nannan||Chen, Yen-Lun

publication date

  • September 2013

publisher