FastTrack: A Highly Efficient and Generic GPU-Based Multi-object Tracking Method with Parallel Kalman Filter

Abstract

The Kalman Filter based on uniform assumption has been a crucial motion estimation module in trackers. However, it has limitations in non-uniform motion modeling and computational efficiency when applied to large-scale object tracking scenarios. To address these issues, we propose a novel Parallel Kalman Filter (PKF), which simplifies conventional state variables to reduces computational load and enable effective non-uniform modeling. Within PKF, we propose a non-uniform formulation which models non-uniform motion as uniform motion by transforming the time interval \(\Delta t\) from a constant into a variable related to displacement, and incorporate a deceleration strategy into the control-input model of the formulation to tackle the escape problem in Multi-Object Tracking (MOT); an innovative parallel computation method is also proposed, which transposes the computation graph of PKF from the matrix to the quadratic form, significantly reducing the computational load and facilitating parallel computation between distinct tracklets via CUDA, thus making the time consumption of PKF independent of the input tracklet scale, i.e., O(1). Based on PKF, we introduce Fast, the first fully GPU-based tracker paradigm, which significantly enhances tracking efficiency in large-scale object tracking scenarios; and FastTrack, the MOT system composed of Fast and a general detector, offering high efficiency and generality. Within FastTrack, Fast only requires bounding boxes with scores and class ids for a single association during one iteration, and introduces innovative GPU-based tracking modules, such as an efficient GPU 2D-array data structure for tracklet management, a novel cost matrix implemented in CUDA for automatic association priority determination, a new association metric called HIoU, and the first implementation of the Auction Algorithm in CUDA for the asymmetric assignment problem. Experiments show that the average time per iteration of PKF on a GTX 1080Ti is only 0.2 ms; Fast can achieve a real-time efficiency of 250FPS on a GTX 1080Ti and 42FPS even on a Jetson AGX Xavier, outperforming conventional CPU-based trackers. Concurrently, FastTrack demonstrates state-of-the-art performance on four public benchmarks, specifically MOT17, MOT20, KITTI, and DanceTrack, and attains the highest speed in large-scale tracking scenarios of MOT20.