ST-TrackNet: A Multiple-Object Tracking Network Using Spatio-Temporal Information

Abstract

Multiple-object tracking (MOT) is a crucial component in autonomous driving systems. However, inaccurate object detection is always the bottleneck for MOT. Most detectors are not designed to take the temporal information across consecutive frames into consideration. To take advantage of such information, we design a novel data representation, the spatio-temporal (ST) map, which collects a batch of detection results spatio-temporally, and we train a novel network, ST-TrackNet, to assign predicted track IDs to each positive detection across a sequence. With our ST map detection fed into the tracker, the correlation of objects between adjacent frames becomes prominent, which improves the performance of the tracker in the data association step. Moreover, the long-term trajectory in a sequence also helps to refine the detection results. We train and evaluate our network on the KITTI dataset, a CARLA simulation dataset, and a dataset recorded in a factory environment. Our approach generally achieves superior performance over the state-of-the-art. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —We investigate the MOT problem in this paper. A spatio-temporal pipeline is proposed to provide a solution to this problem. Object detection results produced by off-the-shelf object detectors are used to form the proposed ST maps. In low signal-to-noise ratio (SNR) situations, our proposed framework can achieve more accurate and robust tracking results with more false-positives. Due to the simplicity and modular design of our framework, it can be applied directly after the detection stage to achieve the online tracking task. The proposed method is evaluated on several datasets, and the experimental results demonstrate its effectiveness. Our method can also be used for other autonomous driving applications, such as path planning and trajectory prediction.