Crowd Abnormality Detection and Localization
Vikas Gupta, Soma Biswas · 2014
Abnormality detection in crowded scenes plays a very important role in automatic monitoring of surveillance feeds. Here we present a novel framework for abnormality detection in crowd videos. The key idea of the approach is that rarely or sparsely occurring events correspond to abnormal activities while the commonly occurring events correspond to the normal activities. Given an input video, multiple feature matrices are computed which are decomposed into their low-rank and sparse components, out of which the sparse components correspond to the abnormal activities. The approach does not require any explicit modeling of crowd behavior or training. Localization of the anomalies is obtained as a by-product of the proposed approach by doing an inverse mapping between the entries of the matrix and the pixels in the video frames. The method is very general and can be applied for both sparsely crowded as well as densely crowded scenes and it can be used to detect both global and local abnormalities. Experimental evaluation on two widely used datasets as well as some dense crowd videos downloaded from the web shows the effectiveness of the proposed approach. Comparison with several state-of-the-art crowd abnormality detection approaches show that the proposed method compares well as compared to the other approaches.