Abstract
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 language | English |
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Title of host publication | Proceedings of the 25th Computer Vision Winter Workshop (CVWW) |
Place of Publication | Ljubljana |
Publisher | Slovenian Pattern Recognition Society |
Pages | 27-36 |
Publication status | Published - 2020 |
Event | 25th Computer Vision Winter Workshop: CVWW 2020 - Rogaška Slatina, Rogaska Slatina, Slovenia Duration: 3 Feb 2020 → 5 Feb 2020 https://cvww2020.vicos.si/ |
Conference
Conference | 25th Computer Vision Winter Workshop |
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Abbreviated title | CVWW 2020 |
Country/Territory | Slovenia |
City | Rogaska Slatina |
Period | 3/02/20 → 5/02/20 |
Internet address |