Visual tracking by adaptive kalman filtering and mean shift
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
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In this paper, we proposed an enhanced kernelbased algorithm for visual tracking based on the video sequences captured from a fixed camera on the top of the scene. The technique presented here employs the image color intensity information and the Local Binary Pattern (LBP) to construct a fourdimensional histogram representative of the color intensity values and the texture of the target under study. The new location is then determined by Mean Shift iteration after the predict location is confirmed by Kalman filter. Color, texture, and motion features are integrated to track objects. Large numbers of experiments on video sequences in different scenes has demonstrated its accuracy and robustness.