Classifying Global Scene Context for On-line Multiple Tracker Selection

Salma Moujtahid, Stefan Duffner and Atilla Baskurt

Abstract

In this paper, we present a novel framework for combining several independent on-line trackers using the visual scene context. The aim of our method is to decide automatically at each point in time which specific tracking algorithm works best under the given scene or acquisition conditions. To this end, we define a set of generic global context features computed on each frame of a set of training videos. At the same time, we record the performance of each individual tracker on these videos in terms of object bounding box overlap with the ground truth. Then a classifier is trained to estimate which tracker gives the best result given the global scene context in a particular frame. We experimentally showed that such a classifier can predict the best tracker with a precision of over 80% in unknown videos with unknown environments. The proposed tracking method further filters the classifier responses temporarily using a Hidden Markov Model in order to avoid rapid oscillations between different trackers. Finally, we evaluated the overall tracking system and showed that this scene context-based tracker selection considerably improves the overall robustness and compares favourably with the state-of-the-art.

Session

Poster 2

Files

Extended Abstract (PDF, 436K)
Paper (PDF, 555K)

DOI

10.5244/C.29.163
https://dx.doi.org/10.5244/C.29.163

Citation

Bibtex

@inproceedings{BMVC2015_163,
	title={Classifying Global Scene Context for On-line Multiple Tracker Selection},
	author={Salma Moujtahid and Stefan Duffner and Atilla Baskurt},
	year={2015},
	month={September},
	pages={163.1-163.12},
	articleno={163},
	numpages={12},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.163},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.163}
}