Self-organizing Map-Based Object Tracking with Saliency Map and K-Means Segmentation

In this paper, a new method is presented for long-term object tracking in surveillance videos. The developed method combines surrounding image sampling, saliency map, self-organizing map neural network, k-Means segmentation and similarity measurement. Saliency map can provide valuable information to reduce over-segmentation. The surrounding image sampling always extracts the regions which are close to the centroid of the latest tracked target. The self-organizing map quantizes the image samples into a topological space, it compresses information while preserving the most important topological and metric relationships of the primary features. The k-Means algorithm will generate segmentation based on the output of the self-organizing map. Then, according to the segmentation results of the new frame and the first frame, a similarity measurement is used to get the most similar image sample to the specified object in the first frame and thus object position in new frame is found. We apply the developed method to track objects in the real-world environment of surveillance videos, computer simulations indicate that the proposed approach presents better results than those obtained by a direct method approach.