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Detection of highly articulated moving objects by using co-segmentation with application to athletic video sequences

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Abstract

In this paper, we adapt the co-segmentation for the fundamental problem of segmenting automatically and accurately highly articulated athletes in a large variety of poses without any initialization or prior knowledge. Our intention is to reduce the complexity of athlete segmentation by formulating it as a constrained 2D pair of frames’ co-segmentation, in order to extract the common foreground objects under unconstrained environments without any user input. In fact, the co-segmentation allows to integrate implicitly the temporal information for automatic moving object segmentation without any assumption or prior knowledge on camera motion. The proposed method was applied on various real-world video sequences of athletic sports meetings, and promising results are obtained. Experiments show that suggested method witnessed a significant improvement over background subtraction methods, which are commonly used for athlete segmentation.

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  1. http://www.rsgvideos.com; http://www.olympic.org.

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  1. Walid Barhoumi

    Present address: ISI, 2 Rue Bayrouni, 2080 , Ariana, Tunisia

Authors and Affiliations

  1. Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), RIADI Laboratory, Ariana, Tunisia

    Walid Barhoumi

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  1. Walid Barhoumi
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Correspondence to Walid Barhoumi.

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Barhoumi, W. Detection of highly articulated moving objects by using co-segmentation with application to athletic video sequences. SIViP 9, 1705–1715 (2015). https://doi.org/10.1007/s11760-014-0630-y

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