Journal Paper

This paper presents a method to perform the real-time creation of models that are used to represent aspects of tracked objects in video frames. Object modeling is done during the task of tracking previously unseen selected objects, and both tracking and model creation are implemented using the WiSARD weightless neural network and occur in real time, starting from no prior knowledge. The main purpose of this work is to track an object through camera images and, simultaneously, create a model that describes the presented appearances along with the transitions between each learned aspect. To achieve this goal, an object tracker based on the ClusWiSARD weightless neural network model was used to determine the states that describe the observed objects. In this way, it is possible to obtain a system that capture knowledge about the visual structures of the learned objects, creating relationships between the possible appearances, and being able to transit over the model aspects in an appropriate way. Furthermore, the created models have visual representations that can be used to show the learned aspects and validate the state transitions, in addition to being able to visualize occluded parts of objects.