Context-based tracking of object features

In this work, we present a context-based model for tracking object features. More specifically, the context is defined as a collection of features within the local region that surrounds the feature that is being tracked. The model does not rely on any knowledge about the object, and therefore the collection of contextual features in one frame is just a hypothesis that is then reconfirmed or rejected in subsequent frames. We applied the model to tracking horizontal and vertical edges extracted from real-time video streams that contain moving vehicles. Our results show that the performance of the system improves as the number of the contextual features increases.