LAUSR

We present a novel algorithm (DAM) for deterministic motion trajectory segmentation by using epipolar geometry and adaptive kernel-scale voting. DAM is based on geometric models and exploits the information derived from superpixels to deterministically construct a set of initial correlation matrices. Then DAM introduces a novel adaptive kernel-scale voting scheme to measure each initial correlation matrix. After that, based on the voting scores, the set of initial correlation matrices is accumulated to generate a discriminant affinity matrix, which is utilized for final grouping. The key characteristic of the DAM is its deterministic nature, i.e., DAM is able to achieve reliable and consistent performance for motion trajectory segmentation without randomness. Experimental results on both several traditional datasets (i.e., $Hopkins 155$ , $Hopkins 12$ , and $MTPV 62$ datasets) and a more realistic and challenging dataset (i.e., $KT3DMoSeg$ ) show the significant superiority of the proposed DAM over several state-of-the-art motion trajectory segmentation algorithms with respect to segmentation accuracy.