CROWD FLOW SEGMENTATION IN COMPRESSED DOMAIN USING CRF


Autoria(s): Kruthiventi, Srinivas SS; Babu, Venkatesh R
Data(s)

2015

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.iisc.ernet.in/53846/1/ICIP_3417_2015.pdf

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.

Publicador

IEEE

Relação

http://arxiv.org/abs/1506.06006

http://eprints.iisc.ernet.in/53846/

Palavras-Chave #Supercomputer Education & Research Centre
Tipo

Conference Proceedings

NonPeerReviewed