Structured learning for crowd motion segmentation
Habib Ullah, Nicola Conci · 2013
In this paper we present a novel method for motion segmentation in crowded scenes, based on statistical modeling for structured prediction using a Conditional Random Field (CRF). As opposed to other conditional Markov models, CRF overcomes the label bias problem, making it suitable for crowd motion analysis. In our method, a grid of particles is initialized on the scene, and advected using optical flow. The particles are exploited to extract motion patterns, used as input priors for CRF training. Furthermore, we exploit min cut/max flow algorithm to remove the residual noise and highlight the main directions of crowd motion. The experimental evaluation is conducted on a set of benchmark video sequences, commonly used for crowd motion analysis, and the obtained results are compared against other state of the art techniques.