Object Tracking By Multi-Cues Spatial Pyramid Matching

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Dong Kai Wang, Huchuan Lu, Yen-Wei Chen
Journal/Conference Name IEEE International Conference on Image Processing
Paper Category
Paper Abstract In this paper, we propose a novel tracking framework, multi-cues spatial pyramid matching (MSPM). Different cues are used to generate a set of probability maps, where the value of each pixel indicates the probability that it belongs to the foreground. Then those probability maps are combined into a single probabilitymap by a weighted linear function. There exist two main contributions. First, a generic probability maps fusion mechanism is proposed. The weights of different probability maps are updated dynamically to maintain local discriminative power, which is achieved by solving a regression problem efficiently. Second, spatial pyramid matching kernel is adopted as a likelihood function, which considers spatial information of object and is able to cope with occlusions naturally. Experiments performed on several challenging public video sequences demonstrate that our proposed framework achieves considerable performance, compared to algorithms with individual cues or equal weights combination, and other state-of-the-art ones.
Date of publication 2010
Code Programming Language MATLAB

Copyright Researcher 2021