SpaRTA tracking across occlusions via partitioning of 3D clouds of points

Any 3D tracking algorithm has to deal with occlusions: multiple targets get so close to each other that the loss of their identities becomes likely, hence potentially affecting the very quality of the data with interrupted trajectories and identity switches. Here, we present a novel tracking method that addresses the problem of occlusions within large groups of featureless objects by means of three steps: i) it represents each target as a cloud of points in 3D; ii) once a 3D cluster corresponding to an occlusion occurs, it defines a partitioning problem by introducing a cost function that uses both attractive and repulsive spatio-temporal proximity links; iii) it minimizes the cost function through a semi-definite optimization technique specifically designed to cope with the presence of multi-minima landscapes. The algorithm is designed to work on 3D data regardless of the experimental method used: multi--camera systems, lidars, radars and RGB-D systems. By performing tests on public data-sets, we show that the new algorithm produces a significant improvement over the state-of-the-art tracking methods, both by reducing the number of identity switches and by increasing the accuracy of the estimated positions of the targets in real space.