Multi-Target Tracking with GPU-Accelerated Data Association Engine

Multi-Target Tracking (MTT) is a challenging problem in the field of data association and sensor data fusion. Many solutions to MTT assume a Markovian nature to the motion of the target to solve the problem and avoid the potential computational complexity. Recently, we have shown that considering a sequence of three time steps and their resulting triplet costs in data association provides a superior solution that better incorporates the kinematic behavior of maneuvering targets. Nevertheless, the triplet costs pose significant computational overhead and scaling challenges. In this paper, we present significant computational advances in a triplet cost-based data association engine for MTT using Graphics Processing Units (GPUs). We achieve this by improving the computational performance of the dual ascent algorithm for dense Multi-Dimensional Assignment Problem (MAP), presented in Vadrevu and Nagi, 2022. Our contributions include: (1) A very fast GPU-accelerated Linear Assignment Problem (LAP) solver that solves an array of tiled LAPs without synchronizing with the CPU, (2) Reduction in computational overheads of triplet costs by using gating and compressed matrix representations, and (3) Computational performance studies that demonstrate the effectiveness of our computational enhancements. Our resulting solution is 5.8 times faster than the current solution without compromising the accuracy.