Women Safety is one of the challenging areas in daily life’s, and it has led to different security problems. Due to security issues, women hesitate to work beyond hours to reach the safety targets. Our proposed system announces the real-time concept of live surveillance tracking system when the Women available in various unprotected places, and its to tracking many through IoT and Deep Learning. It observes and capture images from live streaming and tracks the exact situations or movements where unacceptable situations on dangerous, abnormalities take places. Risk Desk System will track automatically notified with alerts and when there is changes in unacceptable behaviour happens. Current research work mainly focus on the posture estimation to resolve the identified problems through deep learning, and computer vision. It will be capable of identifying persons, recognize their gender, and estimate their situation exactly on live or SOS. It will help in recognizing harmful situations, where a women is left alone or occupied with a group of men, which also does not limit to only night time but daytime too. Additionally, it observes the people rigorously and continuously, providing more data related to the group of people acting in that situation. The system proposed mainly focuses on SOS gestures, alert automatically to concerned dedicated teams on surrounding police station’s , NGOs, existing family relatives with noticeable alerts on timely and live tracking.
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