Online Detection of Unusual Events in Videos via Dynamic Sparse Coding

Real-time unusual event detection in video stream has been a difficult challenge due to the lack of sufficient training information, volatility of the definitions for both normality and abnormality, time constraints, and statistical limitation of the fitness of any parametric models. We propose a fully unsupervised dynamic sparse coding approach for detecting unusual events in videos based on online sparse reconstructibility of query signals from an atomically learned event dictionary, which forms a sparse coding bases. Based on an intuition that usual events in a video are more likely to be reconstructible from an event dictionary, whereas unusual events are not, our algorithm employs a principled convex optimization formulation that allows both a sparse reconstruction code, and an online dictionary to be jointly inferred and updated. Our algorithm is completely unsupervised, making no prior assumptions of what unusual events may look like and the settings of the cameras. The fac...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Bin Zhao, Li Fei-Fei, Eric Xing
• Keywords: Computer Vision, CVPR 2011, Event Dictionary, Sparse, Unusual Events