FMRF

In this work, a novel region matching based motion estimation scheme to detect objects with accurate boundaries from videos captured by moving camera is developed. Here a fuzzy edge incorporated Markov Random Field (MRF) model is considered for spatial segmentation. The algorithm is able to identify even the blurred boundaries of objects in a scene. Expectation Maximization (EM) algorithm is used to estimate the MRF model parameters. To reduce the complexity of searching, a new scheme is proposed to get a rough idea of maximum possible shift of objects from one frame to another by finding the amount of shift in positions of the centroid. We propose a chi-square test based local histogram matching scheme for detecting moving objects from complex scenes from low illumination environment and objects which change size from one frame to another.

Publication:

1. B. N. Subudhi & A. Ghosh, Moving Objects Detection from Video Sequences using Fuzzy Edge Incorporated Markov Random Field Modeling and Local Histogram Matching ",Proceedings of 4th International Conference on Pattern Recognition and Machine Intelligence (Published by Springer LNCS) ,pp. 173-179, 2011. (pdf)

2. A. Ghosh, B. N. Subudhi and S. Ghosh, "Object Detection from Videos Captured by Moving Camera by using Fuzzy Edge incorporated Markov Random Field and Local Histogram Matching", IEEE Transactions on Circuits and Systems for Video Technology, vol 22, no. 8, pp. 1127-1135, 2012. (pdf)