FotoFile: a consumer multimedia organization and retrieval system
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Detection, Tracking, and Pose Estimation of Faces in Video Streams
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Tracking Head Yaw by Interpolation of Template Responses
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Multiple Object Tracking with Kernel Particle Filter
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Snake Head Boundary Extraction Using Global and Local Energy Minimisation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Multiple appearance models for face tracking in surveillance videos
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Robust shape-based head tracking
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
A generic approach to object matching and tracking
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Real Time Face Detection Based on Motion and Skin Color Information
ISPA '12 Proceedings of the 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications
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This paper presents a new system to achieve face detection and tracking in video sequences. We have performed a combination between detection and tracking modules to overcome the different challenging problems that can occur while detecting or tracking faces. Our proposed system is composed of two modules: Face detection module and face tracking module. In the face detection module, we have used skin color and motion information to extract regions of interest and cut off false positive face. This filtering step has enhanced the next face tracking processing step, as it helps to avoid tracking false positive faces. Regarding tracking module, we have used face detection results to keep the face tracker updated. In order to carry on tracking face we have used particle filter technique which was adapted to track multiple faces. Moreover, each tracked face was described by a defined state: tracked, occluded, entered, left or stopped. The performance of our detect-track system was evaluated using several experiments. This evaluation proved the robustness of our face detection-track system as it supports automatic tracking with no need to manual initialization or re-initialization and reaches best performance to deal with different challenging problems.