Abstract: The invention discloses a correction method and system for object linking in video sequences that are captured by a multiple camera surveillance video system. The invention allows a user to select a specific object to track and for trace correction through an interactive user platform. The interactive user platform shows a video sequence captured by the user-selected camera at the selected time; lists of previous and post video sequences related to the user-selected object or video sequences, and lists of object linking results before and after the selected time. The user refers to the linking results, shown in lists of object linking results before and after the selected time to select one of objects for correction in selected frame of selected video sequence. The selection directs the proposed system to correct the trace of the specific object to track. Henceforth, the correct linking results can be automatically generated.

Abstract: The invention discloses a correction method and system for object linking in video sequences that are captured by a multiple camera surveillance video system. The invention allows a user to select a specific object to track and for trace correction through an interactive user platform. The interactive user platform shows a video sequence captured by the user-selected camera at the selected time; lists of previous and post video sequences related to the user-selected object or video sequences, and lists of object linking results before and after the selected time. The user refers to the linking results, shown in lists of object linking results before and after the selected time to select one of objects for correction in selected frame of selected video sequence. The selection directs the proposed system to correct the trace of the specific object to track. Henceforth, the correct linking results can be automatically generated.

Abstract: Disclosed herein are a system and method for the automatic detection of traffic and parking violations. Camera input is digitally analyzed for vehicle type and location. This information is then processed against local traffic and parking regulations to detect violations. Detectable driving offenses include, but are not limited to: no scooters, buses only, and scooters only lane violations. Detectable parking offenses include, but are not limited to: parking or loitering in bus stops, parking next to fire hydrants, and parking in no-parking zones. Camera input, detected vehicle information, and violations can be stored for later search and retrieval. The system may be configured to signal the authorities or other automated analysis systems about specific violations. When coupled with automatic license plate recognition, vehicles may be automatically matched against a registration database and reported or ticketed.

Abstract: Described herein are a system and a method for abnormal behavior detection using automatic classification of multiple features. Features from various sources, including those extracted from camera input through digital image analysis, are used as input to machine learning algorithms. These algorithms group the features and produce models of normal and abnormal behaviors. Outlying behaviors, such as those identified by their lower frequency, are deemed abnormal. Human supervision may optionally be employed to ensure the accuracy of the models. Once created, these models can be used to automatically classify features as normal or abnormal. This invention is suitable for use in the automatic detection of abnormal traffic behavior such as running of red lights, driving in the wrong lane, or driving against traffic regulations.