CoDe4D: Color-Depth Local Spatio-Temporal Features for Human Activity Recognition From RGB-D Videos

摘要

Human activity recognition has a variety of important real-world applications, such as video analysis, surveillance, and human-robot interaction. As a promising video representation method, local spatio-temporal (LST) features have received increasing attention from computer vision, machine learning, and robotics communities. However, approaches based on traditional LST features only use color information, which face several challenges, such as illumination changes and dynamic backgrounds. The recent availability of commercial color-depth cameras makes it much cheaper, faster, and easier to acquire depth information, which provides a potential to implement more discriminative and robust LST features. In this paper, we introduce the new 4-D color-depth (CoDe4D) LST feature that incorporates both intensity and depth information acquired from RGB-D cameras. Our feature detector constructs a saliency map through applying independent filters in xyzt dimension to represent texture, shape and pose variations, and selects its local maxima as interest points. Our multichannel orientation histogram descriptor applies a 4-D support region, which is adaptive to linear perspective view changes, on each interest point. Then, image gradients of color-depth patches within the support region are computed and quantized using a spherical coordinate-based method to form a final feature vector. We build a complete activity recognition system by combining our features with bag-of-features representations and support vector machines. To evaluate the performance of our CoDe4D LST features and the complete system, we conduct experiments using four benchmark color-depth human activity data sets, including UTK Action3-D, Berkeley MHAD, ACT4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and MSR daily activity 3-D data sets. Experimental results demonstrate the promising representative power of our CoDe4D features, which obtain the state-of-the-art performance on activity recognition from RGB-D visual data.