Title: A multistate pedestrian target recognition and tracking algorithm in public places based on Camshift algorithm

Authors: GaoFeng Han; Yuanquan Zhong

Addresses: Anhui Wenda University of Information Engineering, Hefei, 231201, China ' Anhui Wenda University of Information Engineering, Hefei, 231201, China

Abstract: In order to improve the accuracy of polymorphic pedestrian target recognition and tracking, and shorten tracking time, this paper proposes a public place polymorphic pedestrian target recognition and tracking algorithm based on the Camshift algorithm. Firstly, greyscale the input image and use Hog to select polymorphic pedestrian target features in public places. Then, calculate the probability density of the target area model and construct a pedestrian target recognition and tracking model. Finally, extract the colour features of the target, select the Bhattacharyya coefficient to calculate the similarity between the target model and the candidate model, and use the Camshift algorithm for target recognition, tracking, and matching to obtain the final recognition and tracking results. The experimental results show that the accuracy of the proposed method can reach 97.78 and the operation time is only 0.082 frames/s, indicating that the proposed method effectively improves the target recognition and tracking performance.

Keywords: gamma correction method; Camshift algorithm; HSV space; hue histogram; search window.

DOI: 10.1504/IJBM.2024.140774

International Journal of Biometrics, 2024 Vol.16 No.5, pp.431 - 448

Received: 29 Jun 2023
Accepted: 14 Sep 2023
Published online: 02 Sep 2024 *

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