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Sensors 2016, 16(4), 545;

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68503, USA
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 18 January 2016 / Revised: 11 April 2016 / Accepted: 11 April 2016 / Published: 15 April 2016
(This article belongs to the Section Sensor Networks)
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Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm. View Full-Text
Keywords: visual tracking; exemplar-based detection; convolutional neural network (CNN) features; Gaussian mixture model visual tracking; exemplar-based detection; convolutional neural network (CNN) features; Gaussian mixture model

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Gao, C.; Shi, H.; Yu, J.-G.; Sang, N. Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update. Sensors 2016, 16, 545.

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