Object Tracking Based on Multi-tracker Selection

• Main Authors: Wu, Hsin-Jung, 吳欣蓉
• Other Authors: Lin, Chia-Wen
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/94452648934442508640

碩士 === 國立清華大學 === 電機工程學系 === 102 === In this thesis, we propose a framework which can improve object tracking based on integration of multiple trackers. Object tracking has been attention last decades, but there are not any tracker can be widely adapted to the changing environment in real-world, in our best knowledge. Statistics shows that due to the trackers based on different kind of methods, in the tracking, it will be relatively good and prone to failure in different sequences based on different circumstances. In the past, there are paper used combine many sampled tracker to solve this problem; but there are too many restrictions. Therefore, in this thesis we hope to achieve widespread adapt to changing environment by combining multiple trackers, and then, enhance the performance of object tracking. In step combination of trackers, we will classify trackers into different properties, according to the tracker do well in which environments. Classification includes environmental influence causes tracker failure, such as illumination variation, occlusion, moving camera, and target appearance changes, etc. we combine complementary tracker according to classification of property, in order to achieve the most significant improvement of performance and adapt to the extensive situation. Moreover, to make the appearance evaluation more reliable, features have different weighting according to similarity between the object and the background; it is inversely proportional to the degree of similarity. Among experimental results show the superiorities in our method. First, in the combination of trackers are not restricted to the method. In other words, we can select any tracker to combination, to improve the tracker’s performance; on the other hand, we confirm the selection by tracker, can effectively combine different properties of tracker, to achieve the most significant performance improvement.