Publications (8)
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?
Patrick Dendorfer, Vladimir Yugay, Aljoša Ošep +1
Recent developments in monocular multi-object tracking have been very successful in tracking visible objects and bridging short occlusion gaps, mainly relying on data-driven appear…
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Patrick Dendorfer, Aljoša Ošep, Anton Milan +5
Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not b…
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
Jonathon Luiten, Aljosa Osep, Patrick Dendorfer +4
Multi-Object Tracking (MOT) has been notoriously difficult to evaluate. Previous metrics overemphasize the importance of either detection or association. To address this, we presen…
MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction
Patrick Dendorfer, Sven Elflein, Laura Leal-Taixé
Pedestrian trajectory prediction is challenging due to its uncertain and multimodal nature. While generative adversarial networks can learn a distribution over future trajectories,…
Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation
Patrick Dendorfer, Aljoša Ošep, Laura Leal-Taixé
In this paper, we present Goal-GAN, an interpretable and end-to-end trainable model for human trajectory prediction. Inspired by human navigation, we model the task of trajectory p…
MOTCOM: The Multi-Object Tracking Dataset Complexity Metric
Malte Pedersen, Joakim Bruslund Haurum, Patrick Dendorfer +1
There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences. This lack of metrics decreases explainability, complicates comparison o…