The influence of perceptual grouping on motion detection
Computer Vision and Image Understanding
Video editing based on object movement and camera motion
Proceedings of the working conference on Advanced visual interfaces
Video object tracking using adaptive Kalman filter
Journal of Visual Communication and Image Representation
Track and cut: simultaneous tracking and segmentation of multiple objects with graph cuts
Journal on Image and Video Processing - Video Tracking in Complex Scenes for Surveillance Applications
The influence of perceptual grouping on motion detection
Computer Vision and Image Understanding
Joint tracking and segmentation of objects using graph cuts
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
A multi-resolution framework for multi-object tracking in Daubechies complex wavelet domain
International Journal of Computational Vision and Robotics
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The advance of technology makes video acquisition devices better and less costly, thereby increasing the number of applications that can effectively utilize digital video. Compared to still images, video sequences provide more information about how objects and scenarios change over time. However, video needs more space for storage and wider bandwidth for transmission. Hence is raised the topic of video compression. The MPEG 4 compression standard suggests the usage of object planes. If the object planes are segmented correctly and the motion parameters are derived for each object plane accordingly, a better compression ratio can be expected. Therefore, to take full advantage of the MPEG 4 standard, algorithms for tracking objects are needed. It is also obvious that there is great interest in moving object tracking algorithms in the fields of reconnaissance, robot technology, etc. So, we propose an algorithm to track moving objects in video sequences.The algorithm first separates the moving objects from the background in each frame. Then, four sets of variables are computed based on the positions, the sizes, the grayscale distributions and the presence of textures of the objects. A rule-based method is developed to track the objects between frames, based on the values of the variables. Preliminary experimental results show that the algorithm performs well. The tests also show that the algorithm obtains success in indicating new tracks (object starts moving), ceased tracks (object stops moving) and possible collisions (objects move together).