Multi-expert tracking algorithm based on improved compressive tracker

Yachun Feng; Hong Zhang; Ding Yuan

doi:10.1117/12.2228221

9 December 2015 Multi-expert tracking algorithm based on improved compressive tracker

Yachun Feng, Hong Zhang, Ding Yuan

Proceedings Volume 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015); 981710 (2015) https://doi.org/10.1117/12.2228221
Event: Seventh International Conference on Graphic and Image Processing, 2015, Singapore, Singapore

Abstract

Object tracking is a challenging task in computer vision. Most state-of-the-art methods maintain an object model and update the object model by using new examples obtained incoming frames in order to deal with the variation in the appearance. It will inevitably introduce the model drift problem into the object model updating frame-by-frame without any censorship mechanism. In this paper, we adopt a multi-expert tracking framework, which is able to correct the effect of bad updates after they happened such as the bad updates caused by the severe occlusion. Hence, the proposed framework exactly has the ability which a robust tracking method should process. The expert ensemble is constructed of a base tracker and its formal snapshot. The tracking result is produced by the current tracker that is selected by means of a simple loss function. We adopt an improved compressive tracker as the base tracker in our work and modify it to fit the multi-expert framework. The proposed multi-expert tracking algorithm significantly improves the robustness of the base tracker, especially in the scenes with frequent occlusions and illumination variations. Experiments on challenging video sequences with comparisons to several state-of-the-art trackers demonstrate the effectiveness of our method and our tracking algorithm can run at real-time.

Citation Download Citation

Yachun Feng, Hong Zhang, and Ding Yuan "Multi-expert tracking algorithm based on improved compressive tracker", Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 981710 (9 December 2015); https://doi.org/10.1117/12.2228221

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KEYWORDS

Detection and tracking algorithms

Visual process modeling

Particle filters

Computer vision technology

Machine vision

Video

Image compression

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