Dominant Flow Extraction and Analysis in Traffic Surveillance Videos

Kruthiventi, Srinivas SS and Babu, Venkatesh R (2015) Dominant Flow Extraction and Analysis in Traffic Surveillance Videos. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), JUN 07-12, 2015, Boston, MA.

PDF IEEE_Con_Com_Vis_Pat_Rec_Wor_63_2015.pdf Restricted to Registered users only Download (895kB) | Request a copy

Abstract

Flow analysis of crowd and traffic videos is an important video surveillance task. In this work, we propose an algorithm for long-term flow segmentation and dominant flow extraction in traffic videos. Each flow segment is a temporal sequence of image segments indicating the motion of a vehicle in the camera view. This flow segmentation is done in the framework of Conditional Random Fields using motion and color features. We also propose a distance measure between any two flow segments based on Dynamic Time Warping and use this distance for clustering the flow segments into dominant flows. We then model each dominant flow by generating a representative flow segment, which is the mean of all the time-warped flow segments belonging to its cluster. Using these dominant flow models, we perform path prediction for the vehicles entering the view and detect anomalous motions. Experimental evaluation on a diverse set of challenging traffic videos demonstrates the effectiveness of the proposed method.

Item Type:	Conference Proceedings
Series.:	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	19 Aug 2016 06:13
Last Modified:	19 Aug 2016 06:13
URI:	http://eprints.iisc.ac.in/id/eprint/54384

Actions (login required)

View Item