Pedestrian detection using a moving camera: A novel framework for foreground detection. (May 2020)

• Record Type: Journal Article
• Authors: Ben Khalifa, Anouar; Alouani, Ihsen; Mahjoub, Mohamed Ali; Ben Amara, Najoua Essoukri
• Is Part Of: Cognitive systems research. Volume 60(2020)
• Page Start: 77
• Page End: 96
• Publication Date: 2020-05
• Subjects: Moving pedestrian -- Moving camera -- Ego-motion compensation -- Trajectory classification -- Pedestrian detection challenge; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2019.12.003
• Languages: English
• ISSNs: 1389-0417

Abstract

Highlights: We propose a novel framework based on trajectory classification method for pedestrian detection using a moving camera. We study the main challenges facing pedestrian detection systems as well as methods proposed to handle these challenges. We characterize the main methods of moving pedestrian detection in the case of moving camera. Abstract: While background subtraction techniques have been widely applied to detect moving objects in a video stream captured by a static camera, detecting moving objects using a moving camera still represents a challenging task. In this context, pedestrian detection using a camera placed on the top of a vehicle's windshield has been rarely investigated. This is mainly due to the background ego-motion. Since the scene captured by the camera seems in motion, it is very difficult to distinguish the moving pedestrians from the others that belong to the static part of the scene. For this reason, a compensation step is needed to suppress the ego-motion. This paper presents a study on the main challenges facing pedestrian detection systems as well as methods proposed to handle these challenges. A novel trajectory classification framework for detecting pedestrians even in challenging real-world environments is proposed. The proposed method models the background motion between two consecutive frames in order to compensate the camera motion. Then, it defines a classification process that differentiates between the background and the foreground in the frame. Using the defined foreground, we consequently identify the presence of pedestrians in the scene. The proposed method was validated on a public benchmark dataset: CVC-14 containing both visible and far infrared video sequences in day and night times. Experimental results confirm the effectiveness of the proposed approach in capturing the dynamic aspect between frames and therefore detecting the presence of pedestrians in the scene.