Abstract: An experience-aware anomaly processing system and a method for an experience-aware anomaly processing system are provided. The experience-aware anomaly processing system comprises an anomaly detection module configured to receive geographic location data with corresponding time information of a target object, and analyze target object behavior based on the geographic location data with corresponding time information of the target object; a user feedback module configured to receive user feedback from a user and model user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and a decision module configured to receive user setting from the user, and make a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting.

Abstract: An experience-aware anomaly processing system and a method for an experience-aware anomaly processing system are provided. The experience-aware anomaly processing system comprises an anomaly detection module configured to receive geographic location data with corresponding time information of a target object, and analyze target object behavior based on the geographic location data with corresponding time information of the target object; a user feedback module configured to receive user feedback from a user and model user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and a decision module configured to receive user setting from the user, and make a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting.

Abstract: In accordance with various embodiments of the disclosed subject matter, a method and a system for vision-centric deep-learning-based road situation analysis are provided. The method can include: receiving real-time traffic environment visual input from a camera; determining, using a ROLO engine, at least one initial region of interest from the real-time traffic environment visual input by using a CNN training method; verifying the at least one initial region of interest to determine if a detected object in the at least one initial region of interest is a candidate object to be tracked; using LSTMs to track the detected object based on the real-time traffic environment visual input, and predicting a future status of the detected object by using the CNN training method; and determining if a warning signal is to be presented to a driver of a vehicle based on the predicted future status of the detected object.