Determining Radius and Position of a Sphere from a Single Catadioptric Image
Journal of Intelligent and Robotic Systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Discriminative optical flow tensor for video semantic analysis
Computer Vision and Image Understanding
Player Detection and Tracking in Broadcast Tennis Video
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
An intelligent strategy for the automatic detection of highlights in tennis video recordings
Expert Systems with Applications: An International Journal
Content-based attention ranking using visual and contextual attention model for baseball videos
IEEE Transactions on Multimedia - Special issue on integration of context and content
A Hierarchical Semantics-Matching Approach for Sports Video Annotation
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
IEEE Transactions on Circuits and Systems for Video Technology
Multimedia Tools and Applications
A review of vision-based systems for soccer video analysis
Pattern Recognition
Soccer video summarization using enhanced logo detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Soccer video event detection by fusing middle level visual semantics of an event clip
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Highlight events detection in soccer video using HCRF
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Tennis Video 2.0: A new presentation of sports videos with content separation and rendering
Journal of Visual Communication and Image Representation
Bayesian belief network based broadcast sports video indexing
Multimedia Tools and Applications
A template-based baseball video scene classification using efficient playfield segmentation
Multimedia Tools and Applications
Finding the game flow from sports video
J-MRE '11 Proceedings of the 2011 joint ACM workshop on Modeling and representing events
Automatic player detection, tracking and mapping to field model for broadcast soccer videos
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
A dynamic bayesian network based structural learning towards automated handwritten digit recognition
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
DBN-based structural learning and optimisation for automated handwritten character recognition
Pattern Recognition Letters
HMM based soccer video event detection using enhanced mid-level semantic
Multimedia Tools and Applications
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Video search and indexing with reinforcement agent for interactive multimedia services
ACM Transactions on Embedded Computing Systems (TECS) - Special issue on embedded systems for interactive multimedia services (ES-IMS)
International Journal of Computer Vision
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Video content categorization using the double decomposition
Multimedia Tools and Applications
Image and Vision Computing
Features extraction for soccer video semantic analysis: current achievements and remaining issues
Artificial Intelligence Review
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Video semantic analysis is formulated based on the low-level image features and the high-level knowledge which is encoded in abstract, nongeometric representations. This paper introduces a semantic analysis system based on Bayesian network (BN) and dynamic Bayesian network (DBN). It is validated in the particular domain of soccer game videos. Based on BN/DBN, it can identify the special events in soccer games such as goal event, corner kick event, penalty kick event, and card event. The video analyzer extracts the low-level evidences, whereas the semantic analyzer uses BN/DBN to interpret the high-level semantics. Different from previous shot-based semantic analysis approaches, the proposed semantic analysis is frame-based for each input frame, it provides the current semantics of the event nodes as well as the hidden nodes. Another contribution is that the BN and DBN are automatically generated by the training process instead of determined by ad hoc. The last contribution is that we introduce a so-called temporal intervening network to improve the accuracy of the semantics output