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Kruthiventi, Srinivas SS and Babu, Venkatesh R (2015) CROWD FLOW SEGMENTATION IN COMPRESSED DOMAIN USING CRF. In: IEEE International Conference on Image Processing (ICIP), SEP 27-30, 2015, Quebec City, CANADA, pp. 3417-3421.

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Official URL: http://arxiv.org/abs/1506.06006


Crowd flow segmentation is an important step in many video surveillance tasks. In this work, we propose an algorithm for segmenting flows in H.264 compressed videos in a completely unsupervised manner. Our algorithm works on motion vectors which can be obtained by partially decoding the compressed video without extracting any additional features. Our approach is based on modelling the motion vector field as a Conditional Random Field (CRF) and obtaining oriented motion segments by finding the optimal labelling which minimises the global energy of CRF. These oriented motion segments are recursively merged based on gradient across their boundaries to obtain the final flow segments. This work in compressed domain can be easily extended to pixel domain by substituting motion vectors with motion based features like optical flow. The proposed algorithm is experimentally evaluated on a standard crowd flow dataset and its superior performance in both accuracy and computational time are demonstrated through quantitative results.

Item Type: Conference Proceedings
Series.: IEEE International Conference on Image Processing ICIP
Publisher: IEEE
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords: Crowd Flow Segmentation; Conditional Random Fields; H.264 Compressed Videos; Compressed Domain Processing
Department/Centre: Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited: 17 May 2016 05:41
Last Modified: 17 May 2016 05:41
URI: http://eprints.iisc.ac.in/id/eprint/53846

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