On the tracking of moving objects in image sequences based on shape feature matching

• Main Authors: HSU, DAVID D., 許大為
• Other Authors: Zhang, Si-En
• Format: Others
• Language: zh-TW
• Published: 1996
• Online Access: http://ndltd.ncl.edu.tw/handle/02933829581147699066

碩士 === 中原大學 === 資訊工程研究所 === 84 === This thesis suggests a method to track moving objects in image sequences which are taken by a stationary camera. In this algorithm, the number of moving objects and their moving direction are unlimited, and their moving paths can be crossed. But the objects must move in a plain, and they can''t rotate themselves. Beside, the objects have to own rigid body and inertia characteristic. The first basic idea of this algorithm is to detect the changing parts of the images. These changing parts are parts of moving objects. We call these changing parts "Moving blob". After determined where are the moving blobs, we need use shape analysis to describe them. Then we set up some constraints of these features to match blobs in two consecutive image frames. These relationship is called "Motion vector". This kind of relationship represent the motion track in a small period of time. As mentioned above, these motion vectors consist of many features, so they are mapped into a high dimension feature space. In this feature space, motion vectors with similar motion behavior and appearance features will be together to form a cluster. So we use cluster analysis to separate them. Obviously, each cluster represent a moving blob. With the same idea, we group moving blobs with similar motion behavior into moving objects. After moving objects are found, we extract the motion track coordinates of the objects. In the end, we use pseudo-inverse method to map image coordinates into real world coordinates. In experiments, we use three different kinds of image sequences to test our algorithm. The algorithm finds out the motion path of objects. But the algorithm has some limitations. First, the algorithm can''t analyze rotational motion. Second, if the light is changing dramatically, the algorithm won''t achieve its goal. The color and texture of moving objects and background may also influence the result greatly. These are what we need to study further to make the algorithm more robust.