NDLI: Target Identity-aware Network Flow for online multiple target tracking

Content Provider	IEEE Xplore Digital Library
Author	Dehghan, A. Yicong Tian Torr, P.H.S. Shah, M.
Copyright Year	2015
Description	Author affiliation: Center for Res. in Comput. Vision, Univ. of Central Florida, Orlando, FL, USA (Dehghan, A.; Yicong Tian; Shah, M.) \|\| Dept. of Eng. Sci., Univ. of Oxford, Oxford, UK (Torr, P.H.S.)
Abstract	In this paper we show that multiple object tracking (MOT) can be formulated in a framework, where the detection and data-association are performed simultaneously. Our method allows us to overcome the confinements of data association based MOT approaches; where the performance is dependent on the object detection results provided at input level. At the core of our method lies structured learning which learns a model for each target and infers the best location of all targets simultaneously in a video clip. The inference of our structured learning is done through a new Target Identity-aware Network Flow (TINF), where each node in the network encodes the probability of each target identity belonging to that node. The proposed Lagrangian relaxation optimization finds the high quality solution to the network. During optimization a soft spatial constraint is enforced between the nodes of the graph which helps reducing the ambiguity caused by nearby targets with similar appearance in crowded scenarios. We show that automatically detecting and tracking targets in a single framework can help resolve the ambiguities due to frequent occlusion and heavy articulation of targets. Our experiments involve challenging yet distinct datasets and show that our method can achieve results better than the state-of-art.
Starting Page	1146
Ending Page	1154
File Size	1001162
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298718
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Detectors Yttrium Optimization Lagrangian functions Linear programming Object tracking
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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