Journal of Signal Processing Systems

, Volume 65, Issue 1, pp 49–62

Automatic Detection of Object of Interest and Tracking in Active Video

Authors

    • Vision and Media Lab, School of Computing ScienceSimon Fraser University
  • Ze-Nian Li
    • Vision and Media Lab, School of Computing ScienceSimon Fraser University
Article

DOI: 10.1007/s11265-010-0540-3

Cite this article as:
Huang, J. & Li, Z. J Sign Process Syst (2011) 65: 49. doi:10.1007/s11265-010-0540-3

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.

Keywords

Saliency detectionFeature matchingVisual attentionTrackingActive video

Copyright information

© Springer Science+Business Media, LLC 2010