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In tennis match highlights mainly take place in shots containing full court (Court view shots), therefore successful court view shots detection is useful for highlights extraction. This paper proposes a court view shots detection algorithm, in which shot detection that is the precondition of usual shot classification is given up for shot detection not only costs more time, but also its detection performance...
We explore the benefits of using contextual features for head pose estimation in football games. Contextual features are derived from knowledge of the position of all players and combined with image based features derived from low-resolution footage. Using feature selection and combination techniques, we show that contextual features can aid head pose estimation in football games and potentially be...
How to integrate exercises and entertainments by applying computer technology to make exercises more entertaining, or to make entertainments more fascinating, has become a popular topic in the world. It aims to realize the human-computer interaction by using the technical features of integrating games and exercises. Face-recognition as a key technique in face information processing starts to draw...
In this work is presented a novel approach for the classification of audio concepts in broadcast soccer videos using deep belief network (DBN), a probabilistic neural network with several hidden layers. Comparison with support vector machine (SVM) classifiers has been carried on, showing that our preliminary results are promisingly comparable to the state-of-the-art.
Semantic soccer video analysis has attracted more and more attention recently. In this paper, we present a football event detection method by using multiple feature extraction and fusion. Instead of using low-level features, the proposed method is built upon visual, auditory features, text and audio keywords. Promising event detection results have been achieved. By using the proposed method, we have...
We present a novel learning-based framework for detecting interesting events in soccer videos. The input to the system is a raw soccer video. We have learning at three levels-learning to detect interesting low-level features from image and video data using support vector machines (hereafter, SVMs), and a hierarchical conditional random field- (hereafter, CRF-) based methodology to learn the dependencies...
With the fast development of video semantic analysis, there has been increasing attention to the typical issue of the semantic analysis of soccer program. Based on the color feature analysis, this paper focuses on the video shot segmentation problem from the perspective of semantic analysis, i.e. the semantic shot segmentation. Most existing works segment and classify the shot by using the dominant...
This paper presents a novel semantic-oriented video analysis system for the basketball game videos. Based on Bayesian belief network (BBN), it may bridge this gap between the low-level features describing image/video structure and the high-level knowledge. We apply the support vector machine (SVM) to identify and track the ball, the shooter, and the basket as the low-level features. Based on these...
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