Automatically generating a summary of sports video poses the challenge of detecting interesting moments, or highlights, of a game. Traditional sports video summarization methods leverage editing conventions of broadcast sports video that facilitate the extraction of high-level semantics. However, user-generated videos are not edited, and thus traditional methods are not suitable to generate a summary. In order to solve this problem, this work proposes a novel video summarization method that uses players' actions as a cue to determine the highlights of the original video. A deep neural network-based approach is used to extract two types of action-related features and to classify video segments into interesting or uninteresting parts. The proposed method can be applied to any sports in which games consist of a succession of actions. Especially, this work considers the case of Kendo (Japanese fencing) as an example of a sport to evaluate the proposed method. The method is trained using Kendo videos with ground truth labels that indicate the video highlights. The labels are provided by annotators possessing different experience with respect to Kendo to demonstrate how the proposed method adapts to different needs. The performance of the proposed method is compared with several combinations of different features, and the results show that it outperforms previous summarization methods.
In this paper, we present a novel approach for human action recognition with histograms of 3D joint locations (HOJ3D) as a compact representation of postures. We extract the 3D skelet al joint locations from Kinect depth maps using 's method [6]. The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words, which represent the prototypical poses of actions. The temporal evolutions of those visual words are modeled by discrete hidden Markov models (HMMs). In addition, due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect, our method demonstrates significant view invariance on our 3D action dataset. Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views. Our method is real-time and achieves superior results on the challenging 3D action dataset. We also tested our algorithm on the MSR Action 3D dataset and our algorithm outperforms [25] on most of the cases. With the development of depth sensors, low latency 3D human action recognition has become increasingly important in various interaction systems, where response with minimal latency is a critical process. High latency not only significantly degrades the interaction experience of users, but also makes certain interaction systems, e.g., gesture control or electronic gaming, unattractive. In this paper, we propose a novel active skeleton representation towards low latency human action recognition . First, we encode each limb of the human skeleton into a state through a Markov random field. The active skeleton is then represented by aggregating the encoded features of individual limbs. Finally, we propose a multi-channel multiple instance learning with maximum-pattern-margin to further boost the performance of the existing model. Our method is robust in calculating features related to joint positions, and effective in handling the unsegmented sequences. Experiments on the MSR Action3D, the MSR DailyActivity3D, and the Huawei 3DLife-2013 dataset demonstrate the effectiveness of the model with the proposed novel representation, and its superiority over the state-of-the-art low latency recognition approaches. 