This Type II IDEA project will develop and test a prototype turnkey artificial intelligence aided trespassing detection system.  The system consists of integrated hardware (solar security trailer, networking equipment, etc.) and software that was proven in an earlier project funded by the Federal Transit Administration and Federal Railroad Administration. This system will be developed and tested in collaboration with the industry partner, SunRail, a commuter rail system in the greater Orlando, Florida area. The system hardware will be assembled and installed at selected locations. Data will be collected in those locations for 12 months, and the information will be analyzed to provide actionable safety data to SunRail, the industry collaborator. SunRail will install fencing along their right-of-way. This system could be used to gather trespassing data before and after the fencing installation to evaluate the effectiveness of the solution. At grade crossings, violation data could be used to justify upgrades like the installation of quad gates, gate skirts, or dynamic envelopes based on the types of violations observed. This data can improve trespassing mitigation decision making and support grant applications for further actions. Following this task, sample video data will be collected and analyzed to ensure system accuracy and data quality. The developed system will benefit railroad industry by enabling the collection of previously unavailable trespassing and grade crossing violation information.  It is rather unfeasible to have railroad staff manually annotate video feeds to acquire trespassing data.  This system, on the other hand,  will automatically watch and understand trespass behavior from video feeds at remote locations. Trespass and grade crossing violation information will be aggregated in a trespasser database, presenting users with a video clip of the trespassing event and corresponding metadata (time, weather, type: person, car, motorcycle etc.). Trends and common behaviors can be determined once enough of these events are aggregated.