Changxin Gao1, Huizhang Shi1, Jin-Gang Yu2, Nong Sang1
1National Key Laboratory of Science and Technology on Multispectral Information Processing,
School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
2Department of Computer Science and Engineering,
University of Nebraska-Lincoln, Lincoln, NE, 68503, USA
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.
Source Code
Matlab code(without object model update and background model update). (including off-line background model)
Video results (12 videos).
Plots figures on CVPR2013 benchmark.
Result locations on CVPR2013 benchmark: ELDA_CNN (without object model update and background model update).