Since the shape of a 3D object moving in 3D space changes a lot in 2D image due to translation and rotation, it is very difficult to track the object using the SSD algorithm which finds the matching object in the input image using the template of the moving object. To solve the problem, this paper presents an enhanced SSD algorithm which updates the template based on an extended snake algorithm adaptive to the shape variation. The proposed snake algorithm uses the derivative of the area as the constraint energy to determine the boundary of an interested area considering the progressive variation of the shape. The performance of the proposed algorithm has been proved by the experiments where a mobile robot with one camera tracks a 3D target object translating and rotating arbitrarily in the 3D workspace.
To clearly identify the face given in a surveillance image, this paper proposes a new method that magnifies the face image large enough and brings the magnified face image in focus. For this purpose, the compound lens system consisted of the zooming and focusing lenses is analysed to derive the relationship between the positions of lenses and the image size. Once the face size in the surveillance image and the target face size to achieve are given, the positions of the lenses are determined by the derived relationship. To adjust the positions of the lenses to obtain the focused image, the four point measurement algorithm is proposed. It calculates the focus measures of at most 4 positions and estimates the position having the maximum focus measure. The algorithm has been implemented on the camera system whose lenses are controlled by fast motors. The experimental results have shown that the magnified and focused image can be obtained in 0.77 seconds on average.