Optimization of object region and boundary extraction by energy minimization for activity recognition
Automatic video segmentation for human activity recognition has played an important role in several computer vision applications. Active contour model (ACM) has been used extensively for unsupervised adaptive segmentation and automatic object region and boundary extraction in video sequences. This paper presents optimizing Active Contour Model using recurrent architecture for automatic object region and boundary extraction in human activity video sequences. Taking advantage of the collective computational ability and energy convergence capability of the recurrent architecture, energy function of Active Contour Model is optimized with lower computational time. The system starts with initializing recurrent architecture state based on the initial boundary points and ends up with final contour which represent actual boundary points of human body region. The initial contour of the Active Contour Model is computed using background subtraction based on Gaussian Mixture Model (GMM) such that background model is built dynamically and regularly updated to overcome different challenges including illumination changes, camera oscillations, and changes in background geometry. The recurrent nature is useful for dealing with optimization problems due to its dynamic nature, thus, ensuring convergence of the system. The proposed boundary detection and region extraction can be used for real time processing. This method results in an effective segmentation that is less sensitive to noise and complex environments. Experiments on different databases of human activity show that our method is effective and can be used
for real-time video segmentation.
This paper was published in SPIE Proceedings Vol. 8750
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, Harold H. Szu, Editors, 875008