Video dividing method for high-resolution video regardless of people position

Yasutaka Nakaizumi; Hidehiko Shishido; Yoshinari Kameda

doi:10.1117/12.2590798

13 March 2021 Video dividing method for high-resolution video regardless of people position

Yasutaka Nakaizumi, Hidehiko Shishido, Yoshinari Kameda

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 117663C (2021) https://doi.org/10.1117/12.2590798
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

On detecting people in a video of a wide area, there are three problems. First, the apparent sizes of people are likely to be very different due to their positions in the scene. Second, the density of people is unevenly distributed in the scene. Detecting the entire image implies a high video processing cost. Third, there are regions where people do not permanently appear. These regions should not be processed. To solve these problems, we aim to detect people regardless of people's positions and sizes, focusing on the regions where people could appear. To achieve this aim, we propose a video dividing method based on people's positions and sizes in the video for high-resolution video detection and a method to eliminate the divided regions where people will not appear. We adopt a pyramid representation for video division and integration. We skip processing the regions where no people are detected for a certain amount of time. Furthermore, we use semantic segmentation to eliminate regions where people do not appear permanently. We choose 4K high-resolution videos looking down wide areas from a fixed camera for the experiment. We test the proposed method on PANDA dataset and the videos taken at Tsukuba.

Citation Download Citation

Yasutaka Nakaizumi, Hidehiko Shishido, and Yoshinari Kameda "Video dividing method for high-resolution video regardless of people position", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 117663C (13 March 2021); https://doi.org/10.1117/12.2590798

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