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We address the problem of robust multi-target tracking within the application of hockey player tracking. Although there has been extensive work in multi-target tracking, there is no existing visual tracking system that can automatically and robustly track a variable number of targets and correctly maintain their identities with a monocular camera regardless(More)
This project uses an offline learning algorithm to get a highly efficient classifier for online image retrieval. The boosting algorithm is adopted for the learning process. It chooses a small number (10 in this project) of highly selective features from a very large feature set (there are totally over 45,000 features in the set in this project) and combine(More)
Objective The aim of this project is to find some possible approaches to utilize " Invariant Local Features " [1] for the task of face detection in a single image. Literature study Pattern detection and object recognition are classical tasks in the area of computer vision. They all have many applications. For example, image clustering, object tracking and(More)
In the domain of object recognition, the SIFT feature [1] is known to be a very successful local invariant feature. The performance of the recognition task using SIFT features is very robust and also can be done in real-time. This project present an approach that adopt the SIFT feature for the task of face detection. A feature database is created for the(More)
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