Open Access

ARTICLE

Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

Ali Raza¹, Samia Allaoua Chelloug^2,*, Mohammed Hamad Alatiyyah³, Ahmad Jalal¹, Jeongmin Park⁴

1 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
2 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Sciences and Humanities in Aflaj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
4 Department of Computer Engineering, Tech University of Korea, 237 Sangidaehak-ro, Siheung-si, Gyeonggi-do, 15073, Korea

* Corresponding Author: Samia Allaoua Chelloug. Email:

Computers, Materials & Continua 2023, 75(2), 3275-3289. https://doi.org/10.32604/cmc.2023.029719

Received 10 March 2022; Accepted 09 November 2022; Issue published 31 March 2023

Abstract

Pedestrian detection and tracking are vital elements of today’s surveillance systems, which make daily life safe for humans. Thus, human detection and visualization have become essential inventions in the field of computer vision. Hence, developing a surveillance system with multiple object recognition and tracking, especially in low light and night-time, is still challenging. Therefore, we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night. In particular, we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared (IR) images using machine learning and tracking them using particle filters. Moreover, a random forest classifier is adopted for image segmentation to identify pedestrians in an image. The result of detection is investigated by particle filter to solve pedestrian tracking. Through the extensive experiment, our system shows 93% segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes. Moreover, the system achieved a detection accuracy of 90% using multiple template matching techniques and 81% accuracy for pedestrian tracking. Furthermore, our system can identify that the detected object is a human. Hence, our system provided the best results compared to the state-of-art systems, which proves the effectiveness of the techniques used for image segmentation, classification, and tracking. The presented method is applicable for human detection/tracking, crowd analysis, and monitoring pedestrians in IR video surveillance.

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Keywords

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