Abstract
In the field of computer vision, moving object detection in complicated environments is challenging. This study proposes a moving target detecting algorithm combining ViBe and spatial information to address the poor adaptability of ViBe in complex scenes. The CSLBP texture descriptor was improved to more accurately describe background features. An adaptive threshold was introduced, and thresholding on absolute difference was applied to obtain binary string descriptors using comparisons of pixels from the same region or different images. Afterwards, by adding spatial features to ViBe, a background model based on color and texture feature was obtained. Experimental results show that the proposed method addresses the deficiency of ViBe’s feature representation and improves its adaptability in complex video scenes with shadow, background interference and slow-moving targets. This adaptability allows the precision of detection to improve.
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Acknowledgments
This paper was supported by the NSFC under grant 61303034, the Aeronautical Science Foundation of China under grant 2013ZD31007, and Science and technology project of Shaanxi province (Grant No. 2016GY-033).
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Tian, Y., Wang, D., Jia, P., Liu, J. (2016). Moving Object Detection with ViBe and Texture Feature. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_15
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DOI: https://doi.org/10.1007/978-3-319-48890-5_15
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