Person Detection and Tracking Using Sparse Matrix Measurement for Visual Surveillance

Research in the domain of human processor interaction is associated to target segmentation and tracking. The significant analysis in this sector is encouraged as of a view that the multiple field of applications, consisting of observation, computer–human interaction, will benefit from a vigorous and efficient result. In this paper, we propose a framework for the object tracking using sparse matrix and AdaBoost classifier. The technique includes three steps: at the first stage, we use extracted image features. Later, frame features are represented as a sparse matrix. At the final stage, AdaBoost classifier is used to classify correctly the sparse matrix values based on which the tracking task is performed. Experimental results presented in the paper shows that the framework gives improved performance in comparison with other techniques of object tracking.

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