Lou Xiaoping

A novel approach to sub-pixel corner detection of the grid in camera calibration

In order to improve the precision of camera calibration in the field of computer vision, we have to detect the points of the calibration pattern precisely. A new approach to sub-pixel corner detection of a grid is proposed in this paper, which is based on the combination of Hough Transform and least square fit. The procedure of the approach is as follows: (1) The image is divided into small regions to avoid the influence of camera lens distortion on linear fitting and each region has only one corner definitely. Edges of the grid in each region are detected by the Canny arithmetic operator. (2) Straight lines in each region are detected by Hough Transform. (3) Two straight lines with a certain separation angle are selected arbitrarily in each region as initial location. Then, edge-points are searched and recorded in the neighborhoods of each straight line in view of that the results of Hough Transform may not be sufficient for the edge location in practice. (4) Four straight lines are fitted by least square using the edge-points detected in each region. Center of the quadrilateral formed by the straight lines is calculated as the sub-pixel corner location. Finally, experimental results show that sub-pixel corner location of the grid can be obtained correctly and precisely, and none of them are missed. Consequently, it has been proved that this approach is feasible in the application of sub-pixel corner detection of a grid.

Golf video tracking based on recognition with HOG and spatial–temporal vector

The hand and club movements contain golfer’s swing information, which can be obtained to provide good visualization to be shared on Internet and be summarized in golf studying. In this article, a hand and club tracking framework based on recognition with a complex descriptor combining histograms of oriented gradients and spatial–temporal vector is proposed to obtain their movement trajectories in golf video. After the hand and club are recognized in initial windows defined by the body region, a boosted classifier trained by the proposed descriptor is utilized for recognition and tracking in a searching window predicted by trajectory fitting with previous four object positions. Experiments show that the boosted classifier can have a precision and recall rate both better than 97%, and the hand and club tracking are basically correct in our testing videos.

3D shape acquisition of moving object based on structured light

Belonging to the dynamic and real-time metrology, the key on 3D shape measurement of moving object is one-shot technology including one image acquisition, real-time processing and displaying. According to different speeds of motion, a few structured light methods such as spatial encoding, FTP, phase shifting method and spacetime stereo are introduced and summarized in the form of principle, development, performance and application for shape acquisition of moving object in this paper. Although structured light for measuring 3D shape of moving object is significant and valuable in theory and engineering applications, the research still has some technological challenges and problems to be explored and solved.

Online learning of cascaded classifier designed for multi-object tracking

Visual multi-object tracking is an important task within the field of computer vision. The goal of this paper is to track a variable number of unknown objects in complex scenes automatically using a moving and un-calibrated camera and it devotes to overcome the challenging problems including illumination and scale variations, viewpoint variations and significant occlusions, etc. In this paper, a binary representation containing color and gradient information is utilized to obtain unique features so that the objects can be easily distinguished from each other in the feature space. In addition, an online learning framework based on a cascaded classifier which is trained and updated in each frame to distinguish the object from the background is proposed for long-term tracking. The experimental results on both quantitative evaluations and multi-object tracking show that this approach yields an accurate and robust tracking performance in a large variety of complex scenarios.