POI: Multiple object tracking with high performance detection and appearance feature

314Citations
Citations of this article
264Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Detection and learning based appearance feature play the central role in data association based multiple object tracking (MOT), but most recent MOT works usually ignore them and only focus on the hand-crafted feature and association algorithms. In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting. We make our detection and appearance feature publicly available (https://drive.google.com/open? id=0B5ACiy41McAHMjczS2p0dFg3emM). In the following part, we first summarize the detection and appearance feature, and then introduce our tracker named Person of Interest (POI), which has both online and offline version (We use POI to denote our online tracker and KDNT to denote our offline tracker in submission.).

Cite

CITATION STYLE

APA

Yu, F., Li, W., Li, Q., Liu, Y., Shi, X., & Yan, J. (2016). POI: Multiple object tracking with high performance detection and appearance feature. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9914 LNCS, pp. 36–42). Springer Verlag. https://doi.org/10.1007/978-3-319-48881-3_3

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free