Violence Detection from CCTV Footage Using Optical Flow and Deep Learning in Inconsistent Weather and Lighting Conditions

Madhavan, Rajmohan; Utkarsh,; Vidhya, J.

doi:10.1007/978-3-030-81462-5_56

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNNs extract spatial features, and these features are fed as input to the LSTM to extract temporal features. There are two distinct phases, with the LSTM being fed from the features extracted by the CNN and the LSTM [35,36]. • CNN: Articles that rely exclusively on CNNs for violence detection in videos.…”

Section: Selected Article Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Literature Review of Deep-Learning-Based Detection of Violence in Video

Negre,

Alonso,

González-Briones

et al. 2024

Sensors

View full text Add to dashboard Cite

Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.

show abstract

Section: Selected Article Descriptionmentioning

confidence: 99%

“…The CNN also uses two fully connected layers as a classifier. Madhavan [ 36 ] developed a model, without presenting its performance results. The model was intended to address the challenges associated with classification in inconsistent weather and illumination conditions.…”

Section: Selected Article Descriptionmentioning

confidence: 99%