Texture Based Description of Movements for Activity Analysis

Human motion can be seen as a type of moving texture pattern. In this paper, we propose a novel approach for activity analysis by describing human activities with texture features. Our approach extracts spatially enhanced local binary pattern (LBP) histograms from temporal templates (Motion History Images and Motion Energy Images) and models their temporal behavior with hidden Markov models. The description is useful for action modeling and is suitable for detecting and recognizing various kinds of activities. The method is computationally simple. We perform tests on two published databases and clearly show the good performance of our approach in classification and detection tasks. Furthermore, experimental results show that the approach performs robustly against irregularities in data, such as limping and walking with a dog, partial occlusions and low video quality.