Joint Tracking and Event Analysis for Carried Object Detection

Aryana Tavanai, Muralikrishna Sridhar, Eris Chinellato, Anthony G. Cohn and David C. Hogg

Abstract

This paper proposes a novel method for jointly estimating the track of a moving object and the events in which it participates. The method is intended for dealing with generic objects that are hard to localise and track with the performance of current detection algorithms - our focus is on events involving carried objects. The tracks for other objects with which the target object interacts (e.g. the carrying person) are assumed to be given. The method is posed as maximisation of a posterior probability defined over event sequences and temporally-disjoint subsets of the tracklets from an earlier tracking process. The probability function is a Hidden Markov Model coupled with a term that penalises non-smooth tracks and large gaps in the observed data. We evaluate the method using tracklets output by three state of the art trackers on the new created MINDSEYE2015 dataset and demonstrate improved performance.

Session

Poster 1

Files

Extended Abstract (PDF, 2M)
Paper (PDF, 2M)

DOI

10.5244/C.29.79
https://dx.doi.org/10.5244/C.29.79

Citation

Bibtex

@inproceedings{BMVC2015_79,
	title={Joint Tracking and Event Analysis for Carried Object Detection},
	author={Aryana Tavanai and Muralikrishna Sridhar and Eris Chinellato and Anthony G. Cohn and David C. Hogg},
	year={2015},
	month={September},
	pages={79.1-79.11},
	articleno={79},
	numpages={11},
	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.29.79},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.79}
}