Using local and global object's information to track vehicles in urban scenes

In intelligent transportation systems (ITS's) vehicle tracking is necessary to permit high-level analysis, such as vehicle counting or classification. Nowadays, the need for a precise vehicle behavior analysis is growing mainly in urban intersections. Typical urban traffic scenes contain high-cluttered areas where static and dynamic occlusions take place and objects are missed. Tracking systems relying on monocular cameras are widespread. However, often they are misled by the complicated object interactions occurring in those areas, thus yielding errors in the higher level modules. The real-time tracking system we have conceived relies on an algorithm exploiting local and global information from corner points and whole object's features that allows us to keep track of many different objects in challenging urban scenarios. We assess our results through extensive on-field testing by manually extracting the ground truth from different sequences taken by real world traffic monitoring systems.