Pedestrian detection and tracking using particle filtering

Gaddigoudar, Prateek K.; Balihalli, Tushar R.; Ijantkar, Suprith S.; Iyer, Nalini C.; Maralappanavar, Shruti

doi:10.1109/ccaa.2017.8229782

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…The detection of unexpected activities has recently attracted the attention of researchers in the field of video surveillance systems. In Gaddigoudar et al (2017), the author describes the problem of decoding and simulating behavior in surveillance videos. Violations are detected using likelihood ratio analysis and categories of person actions in unsupervised learning.…”

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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“…On top of pedestrian detection and tracking, other approaches can be developed, for instance, counting people in specific places or human behavior analysis (e.g., how long they stay in a particular area, where they go, etc.) in intelligent video surveillance systems (e.g., [Gaddigoudar et al 2017], [Praveenkumar et al 2022]). Still, these approaches cannot deal with challenging crowded places (occlusions or low-resolution images) or false positives detections; despite these limitations, some applications can get good performance [Liu et al 2021].…”

Section: Pedestrian-oriented Applicationsmentioning

confidence: 99%

Review on Common Techniques for Urban Environment Video Analytics

Velesaca

Suárez

Sappa

et al. 2022

Anais Do III Workshop Brasileiro De Cidades Inteligentes (WBCI 2022)

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This work compiles the different computer vision-based approaches from the state-of-the-art intended for video analytics in urban environments. The manuscript groups the different approaches according to the typical modules present in video analysis, including image preprocessing, object detection, classification, and tracking. This proposed pipeline serves as a basic guide to representing these most representative approaches in this topic of video analysis that will be addressed in this work. Furthermore, the manuscript is not intended to be an exhaustive review of the most advanced approaches, but only a list of common techniques proposed to address recurring problems in this field.

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“…Researchers in a smart video surveillance system have recently become interested in the unusual activity detection. Gaddigoudar et al [17] describe the difficulty of understanding and modelling behavior in surveillance video. In an unsupervised learning framework, violations are discovered using likelihood ratio analysis and pedestrian legal action categories.…”

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 and tracking using particle filtering

Cited by 12 publications

References 3 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

Review on Common Techniques for Urban Environment Video Analytics

Novel approach for pedestrian unusual activity detection in academic environment

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