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Incrementally Detecting Moving Objects in Video with Sparsity and Connectivity

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Abstract

Moving object detection is crucial for cognitive vision-based robot tasks. However, due to noise, dynamic background, variations in illumination, and high frame rate, it is a challenging task to robustly and efficiently detect moving objects in video using the clue of motion. State-of-the-art batch-based methods view a sequence of images as a whole and then model the background and foreground together with the constraints of foreground sparsity and connectivity (smoothness) in a unified framework. But the efficiency of the batch-based methods is very low. State-of-the-art incremental methods model the background by a subspace whose bases are updated frame by frame. However, such incremental methods do not make full use of the foreground sparsity and connectivity. In this paper, we develop an incremental method for detecting moving objects in video. Compared to existing methods, the proposed method not only incrementally models the subspace for background reconstruction but also takes into account the sparsity and connectivity of the foreground. The optimization of the model is very efficient. Experimental results on nine public videos demonstrate that the proposed method is much efficient than the state-of-the-art batch methods and has higher F1-score than the state-of-the-art incremental methods.

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Acknowledgments

This work was supported in part by the National Basic Research Program of China (973 Program) (Grant No. 2014CB340400), the National Natural Science Foundation of China (Grant Nos. 61172121, 61271412, 61472274, and 61222109, 61503274), the Key Research Program of the Chinese Academy of Sciences (Grant No. KGZD-EW-T03), and Excellent Young Scholar of the Tianjin University of Technology and Education (Grant No. RC14-46).

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Authors and Affiliations

  1. School of Electronic Information Engineering, Tianjin University, Tianjin, 300072, People’s Republic of China

    Jing Pan, Xiaoli Li & Yanwei Pang

  2. School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, People’s Republic of China

    Jing Pan

  3. Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119, Shaanxi, People’s Republic of China

    Xuelong Li

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  1. Jing Pan
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Correspondence to Yanwei Pang.

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Conflict of Interest

Jing Pan, Xiaoli Li, Xuelong Li, and Yanwei Pang declare that they have no conflict of interest.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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This article does not contain any studies with human or animal subjects performed by the any of the authors.

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Pan, J., Li, X., Li, X. et al. Incrementally Detecting Moving Objects in Video with Sparsity and Connectivity. Cogn Comput 8, 420–428 (2016). https://doi.org/10.1007/s12559-015-9373-5

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  • DOI: https://doi.org/10.1007/s12559-015-9373-5

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