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The Kernel Based Multiple Instances Learning Algorithm for Object Tracking

, *,†,‡ and
Department of Intelligent Manufacture, Hebei College of Industry and Technology, Shijiazhuang 050091, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Current address: Hebei College of Industry and Technology, Shijiazhuang 050091, China.
Electronics 2018, 7(6), 97; https://doi.org/10.3390/electronics7060097
Received: 24 April 2018 / Revised: 7 June 2018 / Accepted: 13 June 2018 / Published: 16 June 2018
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Abstract

To realize real time object tracking in complex environments, a kernel based MIL (KMIL) algorithm is proposed. The KMIL employs the Gaussian kernel function to deal with the inner product used in the weighted MIL (WMIL) algorithm. The method avoids computing the pos-likely-hood and neg-likely-hood many times, which results in a much faster tracker. To track an object with different motion, the searching areas for cropping the instances are varied according to the object’s size. Furthermore, an adaptive classifier updating strategy is presented to handle with the occlusion, pose variations and illumination changes. A similar score range is defined with respect to two given thresholds and a similar score from the second frame. Then, the learning rate will be set to be a small value when a similar score is out of the range. In contrast, a big learning rate is used. Finally, we compare its performance with that of the state-of-art algorithms on several classical videos. The experimental results show that the presented KMIL algorithm is faster and robust to the partial occlusion, pose variations and illumination changes. View Full-Text
Keywords: object tracking; kernel based MIL algorithm; Gaussian kernel; adaptive classifier updating object tracking; kernel based MIL algorithm; Gaussian kernel; adaptive classifier updating
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Han, T.; Wang, L.; Wen, B. The Kernel Based Multiple Instances Learning Algorithm for Object Tracking. Electronics 2018, 7, 97.

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