Multi-view human activity recognition based on silhouette and uniform rotation invariant local binary patterns

This paper addresses the problem of silhouette-based human activity recognition. Most of the previous work on silhouette based human activity recognition focus on recognition from a single view and ignores the issue of view invariance. In this paper, a system framework has been presented to recognize a view invariant human activity recognition approach that uses both contour-based pose features from silhouettes and uniform rotation local binary patterns for view invariant activity representation. The framework is composed of three consecutive modules: (1) detecting and locating people by background subtraction, (2) combined scale invariant contour-based pose features from silhouettes and uniform rotation invariant local binary patterns (LBP) are extracted, and (3) finally classifying activities of people by Multiclass Support vector machine (SVM) classifier. The rotation invariant nature of uniform LBP provides view invariant recognition of multi-view human activities. We have tested our approach successfully in the indoor and outdoor environment results on four multi-view datasets namely: our own view point dataset, VideoWeb Multi-view dataset [28], i3DPost multi-view dataset [29], and WVU multi-view human action recognition dataset [30]. The experimental results show that the proposed method of multi-view human activity recognition is robust, flexible and efficient.