This report introduces the Intelligent Abnormal Situation Awareness Platform (i-ASAP), a cutting-edge system developed to mitigate trespassing incidents at railroad crossings. The platform employs advanced computer vision and artificial intelligence technologies to monitor and identify trespassers and obstructions in real-time. Using AlphaPose and OpenPose models within a deep learning anomaly detection system, i-ASAP tracks pedestrian behavior to pinpoint anomalies. A newly developed weakly supervised foreground segmentation network extends the platform's detection capabilities to static and moving objects. Validation through custom dataset testing and real-time field trials demonstrate i-ASAP's effectiveness, setting the stage for potential deployment on edge computing devices.