Image-based tracking has been widely used to obtain the position and velocity information of a moving object in a 2-dimensional or 3-dimensional space. However, the tracking process is always affected by reflection noises and blocking obstacles in the environment. This paper provides a robust and optimal algorithm for tracking a moving object on the surface of water. First, we create a matrix to project the image pixels back to the real world coordinate. Second, color and shape tests are used to recognize the object and a vector is used to represent the object. If the object is partially blocked by the obstacles or the reflection from the water surface, the vector is used to predict the position of the body. In the real-time tracking, a Kalman filter is used to optimize the prediction. We tested our algorithm by tracking a submarine on the water surface of a tank. Experimental results show that the visual tracking method is robust to reflection noises and blocking obstacles.
A neuro-inspired visual tracking method based on programmable system-on-chip platform