Towards Data-driven Multi-target Tracking for Autonomous Driving

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


We investigate the potential of recurrentneural networks (RNNs) to improve traditional on-line multi-target tracking of traffic participants froman ego-vehicle perspective. To this end, we builda modular tracking framework, based on interact-ing multiple models (IMM) and unscented Kalmanfilters (UKF). Following the tracking-by-detectionparadigm, we leverage geometric target propertiesprovided by publicly available 3D object detectors.We then train and integrate two RNNs: A state pre-diction network replaces hand-crafted motion mod-els in our filters and a data association network findsdetection-to-track assignment probabilities. In ourextensive evaluation on the publicly available KITTIdataset we show that our trained models achievecompetitive results and are significantly more robustin the case of unreliable object detections.
Original languageEnglish
Title of host publicationProceedings of the 25th Computer Vision Winter Workshop (CVWW)
Place of PublicationLjubljana
PublisherSlovenian Pattern Recognition Society
Publication statusPublished - 2020
Event25th Computer Vision Winter Workshop: CVWW 2020 - Rogaška Slatina, Rogaska Slatina, Slovenia
Duration: 3 Feb 20205 Feb 2020


Conference25th Computer Vision Winter Workshop
Abbreviated titleCVWW 2020
CityRogaska Slatina
Internet address


Dive into the research topics of 'Towards Data-driven Multi-target Tracking for Autonomous Driving'. Together they form a unique fingerprint.

Cite this