Human activity detection by combining motion descriptors with boosting

A new, combined human activity detection method is proposed. Our method is based on Efros et al.’s motion descriptors and Ke et al.’s event detectors. Since both methods use optical flow, it is easy to combine them. However, the computational cost of the training increases considerably because of the increased number of weak classifiers. We reduce this computational cost by extending Ke et al.’s weak classifiers to incorporate multi-dimensional features. We also introduce a Look Up Table for further high-speed computation. The proposed method is applied to off-air tennis video data, and its performance is evaluated by comparison with the original two methods. Experimental results show that the performance of the proposed method is a good compromise in terms of detection rate and computation time of testing and training.