Automatic recognition of film genres
Proceedings of the third ACM international conference on Multimedia
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Tracking with an Active Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Automatic Video Indexing and Full-Video Search for Object Appearances
Proceedings of the IFIP TC2/WG 2.6 Second Working Conference on Visual Database Systems II
Motion detection with nonstationary background
Machine Vision and Applications
Automatic Genre Identification for Content-Based Video Categorization
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
DETER: detection of events for threat evaluation and recognition
Machine Vision and Applications
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Video genre classification using dynamics
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Automatic Video Classification: A Survey of the Literature
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Statistical models of video structure for content analysis and characterization
IEEE Transactions on Image Processing
Fast motion vector estimation using multiresolution-spatio-temporal correlations
IEEE Transactions on Circuits and Systems for Video Technology
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
Efficient moving object segmentation algorithm using background registration technique
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
With a rapidly dropping price in hand-held cameras and video editing software, user-generated contents are popular these days, especially on online video sharing websites. To facilitate efficient management of large video collections, it is essential to be able to separate amateur video contents from professional ones automatically. In this work, we propose several features that take into account the camera operation and the nature of amateur video clips to achieve this goal. In the proposed scheme, we estimate the number of different cameras being used in a short time interval, the shakiness of the camera, and the distance between the camera and the subject. Experimental results on a test video data set demonstrate that the camera usage can be inferred from the proposed features and reliable separation of professional and amateur video contents can be achieved.