Suspicious Activity Detection Using Live Video Analysis

Gorave, Asmita; Misra, Srinibas; Padir, Omkar; Patil, Anirudha; Ladole, Kshitij

doi:10.1007/978-981-15-0790-8_21

“…Surveillance and Security systems also leverage YOLO for real-time monitoring and rapid identification of suspicious activities [36,37]. By integrating these models into surveillance systems, security personnel can more effectively monitor and respond to potential threats, enhancing public safety [38]. Similarly, in the context of public health measures like social distancing and face mask detection during pandemics [39], YOLO models provided essential support in enforcing health regulations.…”

Section: You Only Look Once Approachmentioning

confidence: 99%

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

Sapkota,

Qureshi,

Flores-Calero

et al. 2024

Preprint

2

0

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This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

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“…Surveillance and Security systems also leverage YOLO for real-time monitoring and rapid identification of suspicious activities [42,43]. By integrating these models into surveillance systems, security personnel can more effectively monitor and respond to potential threats, enhancing public safety [44]. Similarly, in the context of public health measures like social distancing and face mask detection during pandemics [45,46], YOLO models provided essential support in enforcing health regulations.…”

Section: You Only Look Once Approachmentioning

confidence: 99%

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

Sapkota,

Qureshi,

Flores-Calero

et al. 2024

Preprint

0

View full text Add to dashboard Cite

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

show abstract