In this paper, we propose a real-time action recognition algorithm, based on 3D human skeleton positions provided by the depth camera. Our contributions are threefold. First, considering that skeleton positions in different actions at different time are similar, we adopt the Naive-Bayes-Nearest-Neighbor (NBNN) method for classification. Second, to avoid different but similar actions which would decrease recognition rate obviously, we present a hierarchical model and increase the recognition rate significantly. Third, for a real-time application, we apply the sliding window to buffer the input and the threshold presented by the ratio of the second nearest distance and the nearest distance to smooth the output. Our method also rejects undefined actions. Experimental results on the Microsoft Research Action3D dataset demonstrate that our algorithm outperforms other state-of-the-art methods both in recognition rate and computing speed. Our algorithm increases the recognition rate by about 10% at the speed of 30fps averagely (with resolution 640×480).
Real-time accurate motion detection is a key step for many visual applications, such as object detection, smart video surveillance and so on. Although lots of considerable research efforts have been devoted to it, it is still a challenging task due to illumination variation, etc. In order to enhance the robustness to illumination changes, many block-based motion detection algorithms are proposed. However, these methods usually neglect the influences of different block sizes. Furthermore, they cannot choose background-modeling scale automatically as environment changes. These weaknesses limit algorithm’s flexibility and their application scenes. In the paper, we propose a multi-scale motion detection algorithm to benefit from different block sizes. Moreover, an adaptive linear fusion strategy is designed through analyzing the accurateness and robustness of background models at different scales. At detecting, the ratios of different scales would be adjusted as the scene changes. In addition, to reduce the computation cost at each scale, we design an integral image structure for HOG feature of different scales. As a result, all features only need to be computed once. Different out-of-door experiments are tested and demonstrate the performance of proposed model.