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

Sukai Wang, Yuxiang Sun, Zheng Wang, Ming Liu*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

10 Citations (Scopus)

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. © 2022 IEEE.
Original languageEnglish
Pages (from-to)284-295
JournalIEEE Transactions on Automation Science and Engineering
Volume21
Issue number1
Online published31 Oct 2022
DOIs
Publication statusPublished - Jan 2024
Externally publishedYes

Research Keywords

  • Autonomous driving
  • deep learning
  • Detectors
  • Feature extraction
  • Long short term memory
  • multi-object tracking
  • Object detection
  • Point cloud compression
  • spatio-temporal map
  • Tracking
  • Trajectory

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