Towards Data-driven Multi-target Tracking for Autonomous Driving

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband


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.
TitelProceedings of the 25th Computer Vision Winter Workshop (CVWW)
Herausgeber (Verlag)Slovenian Pattern Recognition Society
PublikationsstatusVeröffentlicht - 2020
Veranstaltung25th Computer Vision Winter Workshop: CVWW 2020 - Rogaska Slatina, Slowenien
Dauer: 3 Feb 20205 Feb 2020


Konferenz25th Computer Vision Winter Workshop
KurztitelCVWW 2020
OrtRogaska Slatina

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  • Dieses zitieren

    Fruhwirth-Reisinger, C., Krispel, G., Possegger, H., & Bischof, H. (2020). Towards Data-driven Multi-target Tracking for Autonomous Driving. in Proceedings of the 25th Computer Vision Winter Workshop (CVWW) (S. 27-36). Ljubljana: Slovenian Pattern Recognition Society.