We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm.
"For example, in the field of object recognition, in 2011, Rosenfeld and Weinshall  proposed an algorithm to extract a foreground mask and to identify the locations of objects in the image. In the field of object tracking, in 2012, Wang et al.  used partial least squares (PLS) analysis to label the foreground and background of an image and the results showed that the proposed tracking algorithm was very powerful with the labeled foreground. In the field of content-based image retrieval, in 2006, Shekhar and Chaudhuri  investigated the influence of the foreground. "
[Show abstract][Hide abstract] ABSTRACT: A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG) is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region. Experiments on the given image data set demonstrate the effectiveness of our algorithm.
[Show abstract][Hide abstract] ABSTRACT: There existed many visual tracking methods that are based on sparse representation model, most of them were either generative or discriminative, which made object tracking more difficult when objects have undergone large pose change, illumination variation or partial occlusion. To address this issue, in this paper we propose a collaborative object tracking model with local sparse representation. The key idea of our method is to develop a local sparse representation-based discriminative model (SRDM) and a local sparse representation-based generative model (SRGM). In the SRDM module, the appearance of a target is modeled by local sparse codes that can be formed as training data for a linear classifier to discriminate the target from the background. In the SRGM module, the appearance of the target is represented by sparse coding histogram and a sparse coding-based similarity measure is applied to compute the distance between histograms of a target candidate and the target template. Finally, a collaborative similarity measure is proposed for measuring the difference of the two models, and then the corresponding likelihood of the target candidates is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Experiments on some publicly available benchmarks of video sequences showed that our proposed tracker is robust and effective.
Journal of Visual Communication and Image Representation 01/2013; 25(2). DOI:10.1016/j.jvcir.2013.12.012 · 1.22 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper aims to replicate the operation of an experienced operator for a water supply system. The steering groups of water supply systems face problems due to the decreasing number of experienced operators. Without the skill of experienced operators, it is difficult to carry out safe and stable operation. For this purpose, regression analysis was adapted to replicate the operation of an experienced operator. To resolve the regression problem of knowledge acquisition and decreasing number of experienced operators, partial least squares was used. By using the proposed method, operation in accordance with the state of the water distributions is possible. From the evaluation by using objective functions, it turned out that the operation of the proposed method reflects the policy of the target operation. In addition, experimental results show that the root mean square (RMS) of water level and water conveyances of the proposed method was smaller than RMS of the conventional method. From these results, the proposed method can acquire and regenerate the operation knowledge of an experienced operator.
Industrial Electronics (ISIE), 2013 IEEE International Symposium on; 01/2013
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