Automatic Detection of Object of Interest and Tracking in Active Video

We propose a novel method for automatic detection of Object of Interest (OOI) and tracking from actively acquired videos. The proposed approach benefits the object-centered property of Active Video and facilitates self-initialization in tracking by a non-calibrated camera. We first use a color-saliency weighted Probability-of-Boundary (cPoB) map for keypoints filtering and salient region detection. Successive Classification Maximum Similarities (SCMS) feature matching is used for tracking between two consecutive frames. A strong classifier trained on-the-fly by AdaBoost is utilized for keypoint classification and subsequent Linear Programming rejects outliers. Experiments demonstrate the importance of active video during the data collection phase and confirm that our new approach can automatically detect and reliably track OOI in videos.

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