Person-on-Person Violence Detection in Video Data
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Action movies segmentation and summarization based on tempo analysis
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
SIFT Features Tracking for Video Stabilization
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Detecting Violent Scenes in Movies by Auditory and Visual Cues
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
An Innovative Model of Tempo and Its Application in Action Scene Detection for Movie Analysis
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
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Fighting shots are the highlights of action movies and an effective approach to discriminating fighting shots is very useful for many applications, such as movie trailer construction, movie content filtering, and movie content retrieval. In this paper, we present a novel method for this task. Our approach first extracts the reliable motion information of local invariant features through a robust keypoint tracking computation; then foreground keypoints are distinguished from background keypoints by a sophisticated voting process; further, the parameters of the camera motion model is computed based on the motion information of background keypoints, and this model is then used as a reference to compute the actual motion of foreground keypoints; finally, the corresponding feature vectors are extracted to characterizing the motions of foreground keypoints, and a support vector machine (SVM) classifier is trained based on the extracted feature vectors to discriminate fighting shots. Experimental results on representative action movies show our approach is very effective.