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Traditional point tracking algorithms such as the KLT use local 2Dinformation aggregation for feature detection and tracking, due to which theirperformance 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 ineach frame and matched. This is inefficient and may also lose many points thatare not re-detected in the next frame. We propose a novel tracking algorithm toaccurately and efficiently track CoMaL points. For this, the level line segmentassociated with the CoMaL points is matched to MSER segments in the next frameusing shape-based matching and the matches are further filtered usingtexture-based matching. Experiments show improvements over a simplere-detect-and-match framework as well as KLT in terms of speed/accuracy ondifferent real-world applications, especially at the object boundaries.

CoMaL Tracking: Tracking Points at the Object Boundaries