Fast Object Recognition and Tracking based on Corner Geometric Relationship

• Main Authors: Yu-Hung Ku, 古侑弘
• Other Authors: 陳金聖
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/hss63u

碩士 === 國立臺北科技大學 === 自動化科技研究所 === 98 === This thesis proposes a new computation architecture which can recognize the interesting object very fast. And, a servo tracking system is also proposed to track the moving object accurately. The general object recognition algorithm which uses corner descriptions based on Scale Invariant Feature Transform (SIFT) and Principal Component Analysis (PCA) for patch matching and then eliminate the false matching pairs by Random Sample Consensus (RANSAC) would have very heavy computation load. In this paper, we re-arrange the operating sequence of SIFT, PCA and RANSAC and utilizes the corner geometry relationships to pick up the good initial pairs with robust property. Then, the parameters of the transformation matrix between the recognized image and template image can be solved and all matching pairs can be obtained by the transformation matrix without SIFT-PCA calculation. Only very few SIFT and PCA calculation is required in our algorithm so that it is much faster than the tradition algorithms.From the experimental results, our algorithm only spent around one sixth to seventh computation time comparing to traditional algorithm. In the other hand, our algorithm would have a little poor recognized error, but it is still in the level of good performance in general cases. While the interesting object has been found and recognized, we will like to track this object wherever it moves. In this thesis, we have a servo tracking system which the camera mountained on a platform driven by the servo. This servo system can track the moving object by rotating left and right to keep the object always appears on the center of the screen of the camera approximately. Since our system can work much faster than the conventional system which uses the conventional algorithm to recognize the object, higher data update rate can easily be achieved and better performance can be anticipated in our system. The experimental results can confirm this advantage. An alpha-beta filter is also used in our system to get more accurate data for further applications. It will smooth out the errors originated from the measurement noises and platform moving effectively.