Likelihood learning in modified Dirichlet Process Mixture Model for video analysis

Abstract

Abstract Rapid advancement in machine learning has expedited computer vision-based research applicable to traffic analysis. A 2-stage inference process has been proposed in this paper to learn data distributions applicable to object motion modeling and path learning. In the first stage, a posterior probability learning has been used to get the initial clusters. In the subsequent stage, we use an inference method for likelihood learning by introducing a velocity parameter. It decides the speed at which the model converges to obtain the final clusters. A new sampling method has been proposed that performs better as compared to the Gibbs sampling in terms of computation time. The results demonstrate that the technique has relevance in computer vision applications. The proposed method performs better than the state-of-the-art unsupervised learning methods.