Traditional point tracking algorithms such as the KLT use local 2D
information aggregation for feature detection and tracking, due to which their
performance degrades at the object boundaries that separate multiple objects.
Recently, CoMaL Features have been proposed that handle such a case. However,
they proposed a simple tracking framework where the points are re-detected in
each frame and matched. This is inefficient and may also lose many points that
are not re-detected in the next frame. We propose a novel tracking algorithm to
accurately and efficiently track CoMaL points. For this, the level line segment
associated with the CoMaL points is matched to MSER segments in the next frame
using shape-based matching and the matches are further filtered using
texture-based matching. Experiments show improvements over a simple
re-detect-and-match framework as well as KLT in terms of speed/accuracy on
different real-world applications, especially at the object boundaries.

本文提出了一种基于形状匹配和纹理匹配的新型跟踪算法来精确高效地跟踪 CoMaL 点，并在实验中证明在不同的现实应用中，特别是在物体边界方面相较于简单的重新检测和匹配框架以及 KLT 算法在速度和准确性方面有所提升。