Moving Image Coding Research Articles

A Fast Algorithm Research of Moving Targets Detection Based on GPU

Moving object detection which is an important part of the digital image processing technology is research focus and difficulty in computer vision, pattern recognition, object recognition and tracking, moving image coding, security monitoring and other fields. It also has a wide application prospect in the military, national defense and industry fields. However, when there are large amount of moving targets and exercise conditions, the amount of data generated is also inestimable and target detection is a bottleneck in the data processing at the same time. The work of this paper is to simplify motion detection in optical flow constraint equation solving process and all the solving processes were constructed on GPU for fast speed, then it forms a fast moving target detection.

A new approach to wire-frame tracking for semantic model-based moving image coding

Automatic wire-frame fitting and automatic wire-frame tracking are the two most important and most difficult issues associated with semantic-based moving image coding. A novel approach to high-speed tracking of important facial features is presented as a part of a complete fitting-tracking system we have developed. The method allows real-time processing of head-and-shoulders sequences using software tools only. The algorithm is based on eigenvalue decomposition of the sub-images extracted from subsequent frames of the video sequence. Since each facial feature (the left eye, the right eye, the nose and the lips) is tracked separately, the algorithm can be easily adapted for a parellel machine. The algorithm was tested on numerous widely used head-and-shoulders video sequences containing speaker's head pan, rotation and zoom with remarkably good results. The experiments we have carried out prove that it is possible to maintain tracking even when the facial features are partially occluded.

Facial Motion Analysis for Content-based Video Coding

Automatic wire-frame fitting and automatic wire-frame tracking are the two most important and most difficult issues associated with semantic-based moving image coding. A novel approach to high speed tracking of important facial features is presented as a part of a complete fitting-tracking system. The method allows real-time processing ofhead-and-shoulders sequences using software tools only. The algorithm is based on eigenvalue decomposition of the sub-images extracted from subsequent frames of the video sequence. Each important facial feature (the left eye, the right eye, the nose and the lips) is tracked separately using the same method. The algorithm was tested on widely used head-and-shoulders video sequences containing the speaker's head pan, rotation and zoom with remarkably good results. These experiments prove that it is possible to maintain tracking even when the facial features are partially occluded.

Tracking of the motion of important facial features in model-based coding

A new method of tracking the position of important facial features for semantic wire-frame-based moving-image coding is presented. Reliable and fast tracking of the facial features in head-and-shoulders scenes is of paramount importance for reconstruction of the speaker's motion in videophone systems if wire-frame-based coding is used. The proposed method is based on eigenvalue decomposition of the sub-images extracted from subsequent frames of the video sequence. Motion of each facial feature (the left eye, the right eye, the nose and the lips) is tracked separately. No restrictions other than the presence of the speaker's face, were imposed on the actual contents of the scene. The algorithm was tested on widely used head-and-shoulders video sequences containing moderate head pan, rotation and zoom with remarkably good results. Tracking was maintained even when the facial features were occluded. The algorithm can also be used in other semantic-based systems.

Reliable tracking of facial features in semantic-based video coding

A new method of tracking the position of important facial features for semantic-based moving image coding is presented. Reliable and fast tracking of the facial features in head-and-shoulders scenes is of paramount importance for reconstruction of the speaker's motion in videophone systems. The proposed method is based on eigenvalue decomposition of the sub-images extracted from subsequent frames of the video sequence. Motion of each facial feature (the left eye, the right eye, the nose and the lips) is tracked separately; this means that the algorithm can be easily adapted for a parallel machine. No restrictions, other than the presence of the speaker's face, were imposed on the actual contents of the scene. The algorithm was tested on numerous widely used head-and-shoulders video sequences containing moderate head pan, rotation and zoom, with remarkably good results. Tracking was maintained even when the facial features were occluded. The algorithm can also be used in other semantic-based systems.