Object Tracking via Partial Least Squares Analysis

IEEE Transactions on Image Processing (Impact Factor: 3.63). 06/2012; 21(10):4454-65. DOI: 10.1109/TIP.2012.2205700
Source: PubMed


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.

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    • "For example, in the field of object recognition, in 2011, Rosenfeld and Weinshall [5] 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. [6] 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 [7] investigated the influence of the foreground. "
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