Enhancing change detection in low-quality surveillance footage using markov random fields

Tweed, D. and Ferryman, J. (2008) Enhancing change detection in low-quality surveillance footage using markov random fields. In: 1st ACM workshop on vision networks for behavior analysis , Vancouver, Canada. doi: 10.1145/1461893.1461899

Abstract/Summary

Urban surveillance footage can be of poor quality, partly due to the low quality of the camera and partly due to harsh lighting and heavily reflective scenes. For some computer surveillance tasks very simple change detection is adequate, but sometimes a more detailed change detection mask is desirable, eg, for accurately tracking identity when faced with multiple interacting individuals and in pose-based behaviour recognition. We present a novel technique for enhancing a low-quality change detection into a better segmentation using an image combing estimator in an MRF based model.