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 addition to RGB videos, other methods leverage depth maps obtained from commodity depth sensors (e.g. Microsoft Kinect) to estimate the human 3D pose for action recognition @cite_57 @cite_28 @cite_15 . The third dimension provides robustness to occlusions and variations from the camera viewpoint.
@cite_57 @cite_15 @cite_28
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Human action recognition is an important yet challenging task. The recently developed commodity depth sensors open up new possibilities of dealing with this problem but also present some unique challenges. The depth maps captured by the depth cameras are very noisy and the 3D positions of the tracked joints may be completely wrong if serious occlusions occur, which increases the intra-class variations in the actions. In this paper, an actionlet ensemble model is learnt to represent each action and to capture the intra-class variance. In addition, novel features that are suitable for depth data are proposed. They are robust to noise, invariant to translational and temporal misalignments, and capable of characterizing both the human motion and the human-object interactions. The proposed approach is evaluated on two challenging action recognition datasets captured by commodity depth cameras, and another dataset captured by a MoCap system. The experimental evaluations show that the proposed approach achieves superior performance to the state of the art algorithms. Human action recognition and localization is a challenging vision task with promising applications. To tackle this problem, recently developed commodity depth sensor (e.g., Microsoft Kinect) has opened up new opportunities with several developed human motion features based on depth image for action representation. However, how depth information can be effectively adopted in the middle or high level representation in action detection, in particular, the depth induced three dimensional contextual information for modeling interactions between human-human, human-object and human-surroundings has yet been explored. In this paper, we propose a novel action recognition and localization framework which effectively fuses depth-induced contextual information from different levels of the processing pipeline for understanding various interactions. First, depth image is combined with grayscale image for more robust human subject and object detection. Second, three dimensional spatial and temporal relationship among human subjects or objects is represented based on the combination of grayscale and depth images. Third, depth information is further utilized to represent different types of indoor scenes. Finally, we fuse these multiple stage depth-induced contextual information to yield an unified action detection framework. Extensive experiments on a challenging grayscale + depth human action detection benchmark database demonstrate the effectiveness of the depth-induced contextual information and the high detection accuracy of the proposed framework. Recently developed commodity depth sensors open up new possibilities of dealing with rich descriptors, which capture geometrical features of the observed scene. Here, we propose an original approach to represent geometrical features extracted from depth motion space, which capture both geometric appearance and dynamic of human body simultaneously. In this approach, sequence features are modeled temporally as subspaces lying on the Grassmann manifold. Classification task is carried out via computation of probability density functions on tangent space of each class tacking benefit from the geometric structure of the Grassmann manifold. The experimental evaluation is performed on three existing datasets containing various challenges, including MSR-action 3D, UT-kinect and MSR-Gesture3D. Results reveal that our approach outperforms the state-of-the-art methods, with accuracy of 98.21 on MSR-Gesture3D and 95.25 on UT-kinect, and achieves a competitive performance of 86.21 on MSR-action 3D. 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.