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Automatic Detection of Object of Interest and Tracking in Active Video

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Abstract

We propose a novel method for automatic detection and tracking of Object of Interest (OOI) from actively acquired videos by non-calibrated cameras. The proposed approach benefits from the object-centered property of Active Video and facilitates self-initialization in tracking. We first use a color-saliency weighted Probability-of-Boundary (cPoB) map for keypoint filtering and salient region detection. Successive Classification and Refinement (SCR) 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 solves a maximum similarity problem to reject 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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Notes

  1. The term “tracking” here refers to one of the traditional camera motions in filming, whereas in other parts of this paper it refers to the action of following moving OOI as it is generally used in Computer Vision literatures.

  2. We use the MATLAB code from http://research.graphicon.ru/machine-learning/gml-adaboost-matlab-toolbox.html.

  3. We implement Online Feature Selection (OFS) tracker in MATLAB.

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Acknowledgements

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under the grant RGP36726.

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Authors and Affiliations

  1. Vision and Media Lab, School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada

    Jiawei Huang & Ze-Nian Li

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  1. Jiawei Huang
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  2. Ze-Nian Li
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Correspondence to Jiawei Huang.

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Huang, J., Li, ZN. Automatic Detection of Object of Interest and Tracking in Active Video. J Sign Process Syst 65, 49–62 (2011). https://doi.org/10.1007/s11265-010-0540-3

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