NDLI: Adaptive multiple cues integration for robust outdoor vehicle visual tracking

Content Provider	IEEE Xplore Digital Library
Author	Xin Jing Liu Xiao-dan Ran Bao-jing Liu Ding
Copyright Year	2015
Description	Author affiliation: Xi'an Univ. of Technol., Xi'an, China (Xin Jing; Liu Xiao-dan; Ran Bao-jing; Liu Ding)
Abstract	Aiming at the robust visual tracking for outdoor vehicle, we propose an adaptive multiple cues integration tracking approach in the particle filter framework. The reliability of observation likelihood probability of each cue is estimated according to the uncertainty metric factor of each cue and the spatial distribution of particles with that cue. Then, we compute the integration weight of each cue adaptively according to the reliability of each cue and void ad-hoc tuning the integration weight. Finally, we would obtain the combination likelihood probability of all the observations in an additive integration way. We conduct tracking experiment on three sets of representative outdoor vehicle tracking video sequences to test and compare proposed adaptive multiple cues integration scheme with state-of-the-art approaches. Experimental results demonstrate that our approach is robust to vehicle-colored distracters and partial even full occlusions and outperform the classic approach in the tracking accuracy.
Starting Page	4913
Ending Page	4918
File Size	526598
Page Count	6
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7260402
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Visualization Uncertainty Target tracking visual tracking adaptive integration Color particle filter Reliability uncertainty metric factor multiple cues Vehicles
Content Type	Text
Resource Type	Article

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