Streaming spatio-temporal video segmentation using Gaussian Mixture Model
Dibyendu Mukherjee, Q. M. Jonathan Wu · 2014
Development of an automatic streaming video segmentation method is crucial for many video analysis applications. However, consistency of temporal segmentation and scalability for real-time applications are difficult to achieve. This work proposes a linear-time video segmentation method which is scalable and temporally consistent for streaming videos. A Gaussian Mixture Model (GMM) is used to segment each frame while a recursive filtering updates the parameters of the GMM. This hybrid methodology can uniquely propagate Gaussian clusters through each new frame, update the variance recursively, and create or remove clusters as necessary. In this way, the model automatically manipulates the number of clusters in run-time and adapts to any video sequence over streaming frames maintaining temporal coherence. The method needs a distance threshold value as the main parameter. The creation and removal of new clusters are governed by a cluster similarity criterion that can be based on user-defined distance measure. The experimental results are presented with two possible distance measures. The performance of the proposed method on several datasets is found to be comparable to state-of-the-art video segmentation algorithms.