Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?
Patrick Dendorfer, Vladimir Yugay, Aljoša Ošep, and Laura Leal-Taixé.
Conference on Neural Information Processing Systems (NeurIPS), 2022
MOTCOM: The Multi-Object Tracking Dataset Complexity Metric
Malte Pedersen, Joakim Bruslund Haurum, Patrick Dendorfer and Thomas B. Moeslund.
European Conference on Computer Vision (ECCV), 2022
MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction
Patrick Dendorfer*, Sven Elflein*, and Laura Leal-Taixé.
International Conference on Computer Vision (ICCV), 2021
MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking
Patrick Dendorfer, Aljoša Ošep, Anton Milan, Konrad Schindler, Daniel Cremers, Ian Reid, and Laura Leal-Taixé.
International Journal of Computer Vision (IJCV), 2020
Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation
Patrick Dendorfer, Aljoša Ošep, and Laura Leal-Taixé.
Asian Conference on Computer Vision (ACCV), 2020
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
Jonathon Luiten, Aljoša Ošep, Patrick Dendorfer, Philip Torr, Andreas Geiger, Laura Leal-Taixé, and Bastian Leibe.
International Journal of Computer Vision (IJCV), 2020
MOT20: A benchmark for multi object tracking in crowded scenes
Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, and Laura Leal-Taixé.
arXiv:2003.09003, 2020
CVPR19 Tracking and Detection Challenge: How crowded can it get?
Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, and Laura Leal-Taixé.
arXiv:1906.04567, 2019