Abstract: This paper presents an algorithm for the recognition
and tracking of moving objects, 1/10 scale model car is used to verify
performance of the algorithm. Presented algorithm for the recognition
and tracking of moving objects in the paper is as follows. SURF
algorithm is merged with Lucas-Kanade algorithm. SURF algorithm
has strong performance on contrast, size, rotation changes and it
recognizes objects but it is slow due to many computational
complexities. Processing speed of Lucas-Kanade algorithm is fast but
the recognition of objects is impossible. Its optical flow compares the
previous and current frames so that can track the movement of a pixel.
The fusion algorithm is created in order to solve problems which
occurred using the Kalman Filter to estimate the position and the
accumulated error compensation algorithm was implemented. Kalman
filter is used to create presented algorithm to complement problems
that is occurred when fusion two algorithms. Kalman filter is used to
estimate next location, compensate for the accumulated error. The
resolution of the camera (Vision Sensor) is fixed to be 640x480. To
verify the performance of the fusion algorithm, test is compared to
SURF algorithm under three situations, driving straight, curve, and
recognizing cars behind the obstacles. Situation similar to the actual is
possible using a model vehicle. Proposed fusion algorithm showed
superior performance and accuracy than the existing object
recognition and tracking algorithms. We will improve the performance
of the algorithm, so that you can experiment with the images of the
actual road environment.