Open Access

ARTICLE

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Mahwish Pervaiz¹, Mohammad Shorfuzzaman², Abdulmajeed Alsufyani², Ahmad Jalal³, Suliman A. Alsuhibany⁴, Jeongmin Park^5,*
1 Department of Computer Science, Bahria University, Islamabad, Pakistan
2 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Department of Computer Science, Air University, Islamabad, Pakistan
4 Department of Computer Science, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia
5 Department of Computer Engineering, Tech University of Korea, 237 Sangidaehak-ro, Siheung-si, 15073, Gyeonggi-do, Korea
* Corresponding Author: Jeongmin Park. Email:

Computers, Materials & Continua 2023, 74(1), 841-853. https://doi.org/10.32604/cmc.2023.029629

Received 08 March 2022; Accepted 15 June 2022; Issue published 22 September 2022

Abstract

Crowd management becomes a global concern due to increased population in urban areas. Better management of pedestrians leads to improved use of public places. Behavior of pedestrian’s is a major factor of crowd management in public places. There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment. In this paper, we have proposed a new method for pedestrian’s behavior detection. Kalman filter has been used to detect pedestrian’s using movement based approach. Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded humans. Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes. Gray Wolf Optimizer (GWO) has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient (XG) Boost classifier. Performance has been evaluated using pedestrian’s data from avenue and UBI-Fight datasets, where both have different environment. The mean achieved accuracies are 91.3% and 85.14% over the Avenue and UBI-Fight datasets, respectively. These results are more accurate as compared to other existing methods.

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Keywords

Crowd management; kalman filter; region shrinking; XG-Boost classifier

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