  We present a new combined approach for monocular model-based 3D tracking. A
preliminary object pose is estimated by using a keypoint-based technique. The
pose is then refined by optimizing the contour energy function. The energy
determines the degree of correspondence between the contour of the model
projection and the image edges. It is calculated based on both the intensity
and orientation of the raw image gradient. For optimization, we propose a
technique and search area constraints that allow overcoming the local optima
and taking into account information obtained through keypoint-based pose
estimation. Owing to its combined nature, our method eliminates numerous issues
of keypoint-based and edge-based approaches. We demonstrate the efficiency of
our method by comparing it with state-of-the-art methods on a public benchmark
dataset that includes videos with various lighting conditions, movement
patterns, and speed.

Computer Vision and Pattern Recognition