Pedestrian Detection Based on Background Compensation with Block-Matching Algorithm

Chebli, Khouloud; Khalifa, Anouar Ben

doi:10.1109/ssd.2018.8570499

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…In Chebli and Khalifa (2018), the author describes a technique for counting the number of people using motion patterns for both optical flow and foreground relations. They calculated exponential crowd distribution patterns as well as the dynamic performance of using optical flow to distinguish between walking and running activities (Vasishta et al 2018).…”

Section: Suspicious Activity Detectionmentioning

confidence: 99%

Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map

Gawande,

Hajari,

Golhar

2024

Artif Intell Rev

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Person and suspicious activity detection is a major challenge for image-based surveillance systems. However, the accuracy of person detection is affected by several factors, such as the presence of the person, his trajectory, posture, complex background, and object distortion. In this work, we developed a person-focused dataset that includes various behaviors of students in an educational institution, such as cheating, theft of lab equipment, fights, and threatening situations. This dataset ensures consistent and standardized identification annotations for individuals, making it suitable for detection, tracking, and behavioral analysis of individuals. In addition, we have increased the detection accuracy through an improved architecture called YOLOv5 and introduced an efficient method for detecting global and local anomalous behaviors. This method extracts motion features that accurately describe the person’s movement, speed, and direction. To evaluate the effectiveness of our proposed approach, we validated it against our proposed database and publicly available benchmark datasets. Our method achieves state-of-the-art detection accuracy, namely 96.12%, with an error rate of 6.68% compared to existing methods. The empirical results show a significant improvement in anomalous activity detection. Our paper concludes with a summary and a discussion of possible future research directions.

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Section: Suspicious Activity Detectionmentioning

confidence: 99%

Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map

Gawande,

Hajari,

Golhar

2024

Artif Intell Rev

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“…The author of [41] described a technique for counting the number of people in an image without using a camera. They used an optical-flow motion pattern as well as foreground relationships.…”

Section: Suspicious Activity Detectionmentioning

confidence: 99%

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Gawande¹,

Hajari²,

Golhar

2023

Preprint

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Pedestrian detection and suspicious activity recognition are notable challenges in vision-based surveillance systems. However, the accuracy of pedestrian detection is influenced by a wide range of factors, including human presence, trajectory, posture, complex background, and object deformation. In this paper, we developed a pedestrian dataset that encompasses student behavior in an institution, such as cheating, stealing lab devices, dispute, and threatening scenarios. It provides uniform and steady identification annotations of pedestrians, making it suitable for pedestrian detection, tracking, and behavior detection. In addition, we also improved detection accuracy using the enhanced YOLOv5 architecture. Next, we proposed an efficient method for detection of global and local unusual behaviors. The proposed method extracts motion features that accurately describe pedestrian motion, speed, and direction, etc. The proposed method is validated on a proposed student behavior database and three additional publicly available benchmark datasets. It gives state-of-the-art detection accuracy of 96.12% and miss rate of 6.68% compared to the existing techniques. The experimental results demonstrate a substantial performance improvement in anomalous activity recognition. The final section summarizes and discusses future research directions.

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“…The author used two types of historical and self-history descriptors, as well as neighbouring object histories, to detect abnormalities in a scene [10]. Chebli and Khalifa [20] presented a method for identifying the number of humans in an image without the use of a camera. They exploited foreground relationships as well as an optical-flow motion pattern.…”

Section: Literature Surveymentioning

confidence: 99%

Novel approach for pedestrian unusual activity detection in academic environment

Hajari

Gawande

Golhar

2022

IJ-AI

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<span>In this paper, we propose an efficient method for the detection of student unusual activity in the academic environment. The proposed method extracts motion features that accurately describe the motion characteristics of the pedestrian's movement, velocity, and direction, as well as their intercommunication within a frame. We also use these motion features to detect both global and local anomalous behaviors within the frame. The proposed approach is validated on a newly built proposed student behavior database and three additional publicly available benchmark datasets. When compared to state-of-the-art techniques, the experimental results reveal a considerable performance improvement in anomalous activity recognition. Finally, we summarize and discuss future research directions.</span>

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Pedestrian Detection Based on Background Compensation with Block-Matching Algorithm

Cited by 15 publications

References 19 publications

Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map

Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Novel approach for pedestrian unusual activity detection in academic environment

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