NDLI: A new hybrid tracking strategy based on Pulse Coupled Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Songmin Jia Tao Xu Zhengyin Dong Xiuzhi Li Peng Zhang
Copyright Year	2015
Description	Author affiliation: Coll. of Electron. & Control Eng., Beijing Univ. of Technol., Beijing, China (Songmin Jia; Tao Xu; Zhengyin Dong; Xiuzhi Li; Peng Zhang)
Abstract	Visual object tracking is a fundamental research topic in computer vision. In this paper, we proposed a novel hybrid tracking method based on Pulse Coupled Neural Network (PCNN) and Multiple Instance Learning (MIL). Most modern trackers may be inaccurate when the training samples are imprecise which causes drift. To resolve these problems, MIL method is introduced into the tracking task, which can alleviate drift to some extent. However, the MIL tracker may detect the positive sample that is less important. PCNN is different from traditional artificial neural networks, which can be applied in many image processing fields, such as image segmentation. So, the PCNN was employed as sample detector which can know the most important sample when training the classifier. Then, a more robust and much faster tracker is proposed to approximately maximize the bag likelihood function. Empirical results on a large set of sequences demonstrate the superior performance of the proposed approach in robustness, stability and efficiency to state-of-the-art methods in the literature.
Starting Page	1397
Ending Page	1402
File Size	1215194
Page Count	6
File Format	PDF
e-ISBN	9781467391047
DOI	10.1109/ICInfA.2015.7279505
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Image segmentation Computer vision Target tracking Computational modeling Neurons image segmentation PCNN hybrid tracking strategy Multiple Instance Learning Object tracking sliding window
Content Type	Text
Resource Type	Article

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