Project Description: We are looking for a highly skilled Computer Vision Expert to help design and develop a sophisticated fall detection system using video analysis. This system will be capable of detecting slips and falls in video recordings to improve safety and response strategies in various settings. Responsibilities: Collaborate with our team to design and implement a robust video analysis system. Train, fine-tune, and build machine learning models that can effectively detect specific incidents such as slips and falls in real-time video feeds. Develop functionality for counting the number of people in the video at any given time. Enhance the system’s capabilities to include additional useful features, such as activity recognition and unusual event detection. Requirements: Proven expertise in Computer Vision and machine learning, particularly in the context of video analysis. Extensive experience with neural network architectures, specifically YOLO (You Only Look Once), YOLO NAS, and other similar models. Strong proficiency in programming languages commonly used in AI/ML contexts (e.g., Python, C++). Ability to work with various datasets and adjust models to improve accuracy and efficiency. Excellent problem-solving skills and creativity in developing algorithms to process and analyze video data. Project Goals: The primary goal of this project is to develop a reliable fall detection system that can be integrated into existing video monitoring setups. This system should enhance the ability to quickly respond to incidents and gather analytical insights from the occurrences in the monitored environments.