ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Abnormality detection in crowd videos by tracking sparse components

Biswas, Soma and Gupta, Vikas (2017) Abnormality detection in crowd videos by tracking sparse components. In: MACHINE VISION AND APPLICATIONS, 28 (1-2). pp. 35-48.

[img] PDF
Mac_Vis_App_.28-1_35_2017.pdf - Published Version
Restricted to Registered users only

Download (4MB) | Request a copy
Official URL: http://dx.doi.org/10.1007/s12524-016-0576-3


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 regularly or commonly occurring events correspond to the normal activities. Each input video is represented using feature matrices that capture the nature of activity taking place while maintaining the spatial and temporal structure of the video. The feature matrices are decomposed into their low-rank and sparse components where sparse component corresponds to the abnormal activities. The approach does not require any explicit modeling of crowd behavior or training, but the information from training data can be seamlessly incorporated if it is available. The estimation is further improved by ensuring temporal and spatial coherence of sparse component across the videos using a Kalman filter-like framework. This not only results in reduction of outliers and noise but also fills missing regions in the sparse component. Localization of the anomalies is obtained as a by-product of the proposed approach. Evaluation on the UMN and UCSD datasets and comparisons with several state-of-the-art crowd abnormality detection approaches shows the effectiveness of the proposed approach. We also show results on a challenging crowd dataset created as part of this effort, with videos downloaded from the web.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the SPRINGER, 233 SPRING ST, NEW YORK, NY 10013 USA
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 03 Apr 2017 04:47
Last Modified: 03 Apr 2017 04:47
URI: http://eprints.iisc.ac.in/id/eprint/56441

Actions (login required)

View Item View Item