Barnes Maze Based on Computer Vision and Learning

In this study, motion detection and tracking for real time system is presented. Through the low pass operations, the system works efficiently in real time. In the study, the Barnes Maze test mechanism is automatically learned by Hough transform. Background subtraction algorithms for object detection and estimation approaches based on color, shape and position for tracking are used. Since the desired results are related to the object organs, silhouette analysis is also used. The system observes the experiment mechanism. To detect the target (e.g. cheese), rat motions in the platform are tracked using camera vision system and then the motion positions in 2-dimensional are recorded. These data can be evaluated physically and psychologically. In this study making the learning model of the object from its behaviours is also the future work. For this purpose Markov processes could be used