This project will develop and test a prototype system based on artificial intelligence (AI) technologies for detecting railroad trespassing via image data analytics. Work in Stage 1 will focus on sourcing, acquiring, and assembling the hardware necessary for the prototype design, and initial system setup, as well as generating the initial sample data to support work in Stage 2. .A customized AI-aided video analytics system will be developed, including both the hardware and AI algorithm. Feedback will be sought from the industry partners during this task to ensure critical and relevant data fields will be collected by the software. Sample forward-facing videos from the industry collaborators will be obtained to develop the system. In Stage 2, the AI technology will be tested in rail operational environment. The initial AI algorithm from Stage 1 will continue to be fine-tuned using the collected data. Insights and observations will be drawn upon the data analysis. The technology will be deployed in rail operations in collaboration with the industry collaborator. The system will be installed and evaluated for several months on routes identified by the industry collaborators to meet their needs and best evaluate the technology. The overall accuracy and false detection ratio will be calculated by comparing detection results and manually recognizing data from the selected video records. Trespassing and obstacle data will be used to discover common characteristics and trends. Trespassing analyses could include time series evaluations, geographic mapping, breakdowns by primary violation type, etc. The results will provide practical insights to guide the development of trespassing mitigation strategies. The final report will include all relevant data, methods, models, and conclusions. Guidelines for using the system by the railroad entities will also be provided in the final report.