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Object tracking has been studied for years. The tasks of tracking (retrieving the object to be tracked) is still a challenging problem. In this paper, we present a novel and robust framework for automatic visual object tracking from image sequences or video. The main contribution is a new on-line boosting framework for efficient object tracking from image sequences. Boosting with interactive on-line training allows the object tracker to be trained and improve efficiently. The supervised classifiers are carefull trained on-line in order to increase adaptivity while limiting accumulation of error, i.e. drifting. a framework to solve the drifting and losing of tracking object during tracking. The main idea is to incorporate decision of given by a prior learned strong detector and an on-line adaptation tracking mechanism. The strong detector allows handling complex objects in even complicated environment. The cooperating framework exploits the power of a strong detector and a robust tracker allows to deal with drifting and losing effectively. In the experiments, we demontrate real-time tracking on several challenging sequences, including multi-object tracking of other objects. We outperform other on-line tracking methods especially in case of occlusions and presence of similar objects.

In this paper we present a new approach to online multiple tasks framework using online boosting learning in parallel for object classification in visual objects. The main idea is (a) to learn visual models of object categories require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images; (2) to employ training tasks in parallel while using a shared representation to one class or multiclass classification. What is learned for each task can help other tasks be learned better; (3) to bridge the gap between data acquisition and model building. We demonstrate robustness, efficient and accuracy of the approach on simultaneously online multiple tasks as one-shot learning complex background models, visual tracking, object detection and recognition on benchmark data sets.

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We propose a new method for object detection by combining off-line and on-line boosting learning to classifier grids based on visual information without human intervention concerned to intelligent surveillance system. It allows for combine information labeled and unlabeled use different contexts to update the system, which is not available at off-line training time. The main goal is to develop an adaptive but robust system and to combine prior knowledge with new information in an unsupervised learning framework that is learning 24 hours a day and 7 days a week. We use co-training strategy by combining off-line and on-line learning to the classifier grids. The proposed method is practically favorable as it meets the requirements of real-time performance, accuracy and robustness. It works well with reasonable amount of training samples and is computational efficiency. Experiments on detection of objects in challenging data sets show the outperforming of our approach.