NDLI: Robust Multitask Multiview Tracking in Videos

Content Provider	IEEE Xplore Digital Library
Author	Xue Mei Zhibin Hong Prokhorov, D. Dacheng Tao
Copyright Year	2012
Abstract	Various sparse-representation-based methods have been proposed to solve tracking problems, and most of them employ least squares (LSs) criteria to learn the sparse representation. In many tracking scenarios, traditional LS-based methods may not perform well owing to the presence of heavy-tailed noise. In this paper, we present a tracking approach using an approximate least absolute deviation (LAD)-based multitask multiview sparse learning method to enjoy robustness of LAD and take advantage of multiple types of visual features, such as intensity, color, and texture. The proposed method is integrated in a particle filter framework, where learning the sparse representation for each view of the single particle is regarded as an individual task. The underlying relationship between tasks across different views and different particles is jointly exploited in a unified robust multitask formulation based on LAD. In addition, to capture the frequently emerging outlier tasks, we decompose the representation matrix to two collaborative components that enable a more robust and accurate approximation. We show that the proposed formulation can be effectively approximated by Nesterov's smoothing method and efficiently solved using the accelerated proximal gradient method. The presented tracker is implemented using four types of features and is tested on numerous synthetic sequences and real-world video sequences, including the CVPR2013 tracking benchmark and ALOV++ data set. Both the qualitative and quantitative results demonstrate the superior performance of the proposed approach compared with several state-of-the-art trackers.
Page Count	17
File Size	8890898
Starting Page	2874
Ending Page	2890
File Format	PDF
ISSN	2162237X
Volume Number	26
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Robustness Visualization Sparse matrices Noise Dictionaries Matrix decomposition tracking. L1 minimization least absolute deviation (LAD) multitask multiview sparse representation tracking
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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