The traditional human action recognition (HAR) method is based on RGB video. Recently, with the introduction of Microsoft Kinect and other consumer class depth cameras, HAR based on RGB-D (RGB-Depth) has drawn increasing attention from scholars and industry. Compared with the traditional method, the HAR based on RGB-D has high accuracy and strong robustness. In this paper, using a selective ensemble support vector machine to fuse multimodal features for human action recognition is proposed. The algorithm combines the improved HOG feature-based RGB modal data, the depth motion map-based local binary pattern features (DMM-LBP), and the hybrid joint features (HJF)-based joints modal data. Concomitantly, a frame-based selective ensemble support vector machine classification model (SESVM) is proposed, which effectively integrates the selective ensemble strategy with the selection of SVM base classifiers, thus increasing the differences between the base classifiers. The experimental results have demonstrated that the proposed method is simple, fast, and efficient on public datasets in comparison with other action recognition algorithms.

We consider a two person perfect information game with a buffer. On each round, Player I selects a vectorv∈Rnwith |v|≤1, where |·| is thel2-norm, and Player II can either put the vector in the buffer or choose a sign ϵi=±1 for a given vectorvi. There are no more thandvectors that can be put in the buffer. Player II's object is to keep the cumulative sum |∑ϵivi| as small as possible. We prove that the value of the game goes to infinity ifd≤n−2. We give an upper bound of the value ifd≥n−1. The same results hold for a generalized problem