• Manoj Mandapaka
• Nithin Narayana Varma Mudunuri
• Sai Deep Timadana
• Venkata Sai Goutham Yedla
• Mrs. G. Uma Devi

AI-powered surveillance systems have developed very rapidly and played an important role in enhancing surveillance of public safety. This article suggests a deep learning-based comparative study for gender-based safety within the framework of risk assessment and intervention measures. The research fuses a series of AI algorithms, such as gender classification, emotion detection, SOS detection, lone women tracking, object tracking, and Automatic Number Plate Recognition (ANPR).EfficientNet-B0 is used in gender classification, Deep_Emotion for emotion detection, MediaPipe for body gesture and hand recognition, and YOLOv8 with DeepSORT for object tracking. An extensive evaluation system is used to assess system performance to facilitate real-time decision-making. The research concludes with the comparison of performance in intervention measures, which points towards the necessity of multimodal AI fusion for gender-based risk assessment.

Surveillance, Deep Learning, Comparative Analysis, Gender-Specific, Safety, Risk Assessment, Intervention Strategies, Gender Classification, Emotion Recognition, SOS Detection, Lone Women Tracking, Object Tracking, Deep Emotion, MediaPipe, YOLOv8, DeepSORT

"Deep Learning Based Comparative Analytics For Gender-Specific Safety : Risk Assessment And Intervention Strategies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.c691-c698, March-2025, Available :http://www.jetir.org/papers/JETIR2503283.pdf

"Deep Learning Based Comparative Analytics For Gender-Specific Safety : Risk Assessment And Intervention Strategies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppc691-c698, March-2025, Available at : http://www.jetir.org/papers/JETIR2503283.pdf