Efficient Spatio-Temporal Data Association Using Multidimensional Assignment in Multi-Camera Multi-Target Tracking

This paper proposes a novel multi-target tracking method which jointly solves a data association problem using images from multiple cameras. In this work, the spatiotemporal data association problem is formulated as a multidimensional assignment problem (MDA). To achieve a fast, efficient, and easily implementable approximation algorithm, we solve the MDA problem approximately by solving a sequence of bipartite matching problems using random splitting and merging operations. In this formulation, we design a new cost function, considering the accuracy in 3D reconstruction, motion smoothness, visibility from cameras, starting/ending at entrance and exit zone, and false positive. Our approach reconstructs 3D trajectories that represent people’s movement as 3D cylinders whose locations are estimated considering all adjacent frames. The experiments illustrate the proposed method shows the state-of-the-art performance in challenging multi-camera datasets and the computational efficiency with 8 times faster computation than the existing BIP approach.