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.
In this paper, we have proposed a simple yet robust novel approach for segmentation of high density crowd flows based on super-pixels in H.264 compressed videos. The collective representation of the motion vectors of the compressed video sequence is transformed to color map and super-pixel segmentation is performed at various scales for clustering the coherent motion vectors. The number of dynamically meaningful flow segments is determined by measuring the confidence score of the accumulated multi-scale super-pixel boundaries. The final crowd flow segmentation is obtained from the edges that are consistent across all the super-pixel resolutions. Hence, our major contribution involves obtaining the flow segmentation by clustering the motion vectors and determination of number of flow segments using only motion super-pixels without any prior assumption of the number of flow segments. The proposed approach was bench-marked on standard crowd flow dataset. Experiments demonstrated better accuracy and speedup for the proposed approach compared to the state-of-the-art methods.