1-D integral image for enhancing efficiency and effectiveness of probabilistic occupancy map-based people localization approach

Yen-Shuo Lin; Hua-Tsung Chen; Jenq-Neng Hwang; Ching-Ju Hsiao; Jen-Hui Chuang

doi:10.1117/12.2244978

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29 August 2016 1-D integral image for enhancing efficiency and effectiveness of probabilistic occupancy map-based people localization approach

Yen-Shuo Lin, Hua-Tsung Chen, Jenq-Neng Hwang, Ching-Ju Hsiao, Jen-Hui Chuang

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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 1003327 (2016) https://doi.org/10.1117/12.2244978
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

The popularity of vision-based surveillance systems arouses much research attention in improving the accuracy and efficiency of people localization. Using probabilistic occupancy map (POM) becomes one of the mainstream approaches to people localization due to its great localization accuracy under severe occlusions and lighting changes. However, to enable the usage of rectangular human models and the subsequent 2-D integral image computation, it is assumed that videos are taken at head or eye level. Even so, the computation complexity is still high. Moreover, surveillance videos are often taken from security cameras located at a higher-up location with an oblique viewing angle, so that human models may be quadrilateral and the pixel-based 2-D integral image cannot be utilized. Accordingly, we propose the use of 1-D integral images which are produced for foreground object(s) in an image along equally-spaced line samples originated from the vanishing point of vertical lines (VPVL). Experimental results show that the proposed approach does improve the efficiency and effectiveness of the POM approach in more general camera configurations.

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Yen-Shuo Lin, Hua-Tsung Chen, Jenq-Neng Hwang, Ching-Ju Hsiao, and Jen-Hui Chuang "1-D integral image for enhancing efficiency and effectiveness of probabilistic occupancy map-based people localization approach", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003327 (29 August 2016); https://doi.org/10.1117/12.2244978

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