Visual tracking is an important area in computer vision. How to deal with illumination and occlusion problems
is a challenging issue. This paper presents a novel and efficient tracking algorithm to handle such problems. On one hand, a
target's initial appearance always has clear contour, which is light-invariant and robust to illumination change. On the other
hand, features play an important role in tracking, among which convolutional features have shown favorable performance.
Therefore, we adopt convolved contour features to represent the target appearance. Generally speaking, first-order derivative
edge gradient operators are efficient in detecting contours by convolving them with images. Especially, the Prewitt operator
is more sensitive to horizontal and vertical edges, while the Sobel operator is more sensitive to diagonal edges. Inherently,
Prewitt and Sobel are complementary with each other. Technically speaking, this paper designs two groups of Prewitt and
Sobel edge detectors to extract a set of complete convolutional features, which include horizontal, vertical and diagonal edges
features. In the first frame, contour features are extracted from the target to construct the initial appearance model. After
the analysis of experimental image with these contour features, it can be found that the bright parts often provide more
useful information to describe target characteristics. Therefore, we propose a method to compare the similarity between
candidate sample and our trained model only using bright pixels, which makes our tracker able to deal with partial occlusion
problem. After getting the new target, in order to adapt appearance change, we propose a corresponding online strategy
to incrementally update our model. Experiments show that convolutional features extracted by well-integrated Prewitt and
Sobel edge detectors can be efficient enough to learn robust appearance model. Numerous experimental results on nine
challenging sequences show that our proposed approach is very effective and robust in comparison with the state-of-the-art
trackers.