Robust Feature-based Object Tracking

Object tracking is an important component of many computer vision systems. It is widely used in video surveillance, robotics, 3D image reconstruction, medical imaging, and human computer interface. In this paper, we focus on unsupervised object tracking, i.e., without prior knowledge about the object to be tracked. To address this problem, we take a feature-based approach, i.e., using feature points (or landmark points) to represent objects. Feature-based object tracking consists of feature extraction and feature correspondence. Feature correspondence is particularly challenging since a feature point in one image may have many similar points in another image, resulting in ambiguity in feature correspondence. To resolve the ambiguity, algorithms, which use exhaustive search and correlation over a large neighborhood, have been proposed. However, these algorithms incur high computational complexity, which is not suitable for real-time tracking. In contrast, Tomasi and Kanade’s tracking algorithm only searches corresponding points in a small candidate set, which significantly reduces computational complexity; but the algorithm may lose track of feature points in a long image sequence. To mitigate the limitations of the aforementioned algorithms, this paper proposes an efficient and robust feature-based tracking algorithm. The key idea of our algorithm is as below. For a given target feature point in one frame, we first find a corresponding point in the next frame, which minimizes the sum-of-squared-difference (SSD) between the two points; then we test whether the corresponding point gives large value under the so-called Harris criterion. If not, we further identify a candidate set of feature points in a small neighborhood of the target point; then find a corresponding point from the candidate set, which minimizes the SSD between the two points. The algorithm may output no corresponding point due to disappearance of the target point. Our algorithm is capable of tracking feature points and detecting occlusions/uncovered regions. Experimental results demonstrate the superior performance of the proposed algorithm over the existing methods.