Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes
J-MRE '11 Proceedings of the 2011 joint ACM workshop on Modeling and representing events
Abnormal event detection via multi-instance dictionary learning
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Detecting interesting events using unsupervised density ratio estimation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Dynamic scene understanding by improved sparse topical coding
Pattern Recognition
Abnormal event detection in crowded scenes using sparse representation
Pattern Recognition
Weighted interaction force estimation for abnormality detection in crowd scenes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Abnormal crowd behavior detection and localization using maximum sub-sequence search
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
Computer Vision and Image Understanding
Sparse representation for robust abnormality detection in crowded scenes
Pattern Recognition
Robust action recognition using local motion and group sparsity
Pattern Recognition
Online detection of abnormal events in video streams
Journal of Electrical and Computer Engineering
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We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. To condense the size of the dictionary, a novel dictionary selection method is designed with sparsity consistency constraint. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. Our method provides a unified solution to detect both local abnormal events (LAE) and global abnormal events (GAE). We further extend it to support online abnormal event detection by updating the dictionary incrementally. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our algorithm.