Crowd Flow Segmentation in Compressed Domain using CRF

TL;DR

This paper introduces an unsupervised crowd flow segmentation algorithm that operates directly on motion vectors from compressed videos using CRF modeling, achieving high accuracy and efficiency.

Contribution

It presents a novel method for crowd flow segmentation in compressed domain videos using CRF, eliminating the need for full decoding or additional feature extraction.

Findings

Outperforms existing methods in accuracy

Reduces computational time significantly

Works directly on motion vectors from compressed videos

Abstract

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.