Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks
| Autor: | Bernhard Sick, Viktor Kress, Konrad Doll, Stefan Zernetsch |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
050210 logistics & transportation
0209 industrial biotechnology Network architecture Computer science business.industry 05 social sciences Work (physics) Perspective (graphical) 02 engineering and technology Torso Machine learning computer.software_genre Motion (physics) 020901 industrial engineering & automation medicine.anatomical_structure Recurrent neural network 0502 economics and business Trajectory medicine Artificial intelligence Duration (project management) business computer |
| Zdroj: | Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030687625 ICPR Workshops (1) |
| Popis: | In this work, we use Recurrent Neural Networks (RNNs) in form of Gated Recurrent Unit (GRU) networks to forecast trajectories of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic utilizing the past trajectory and 3D poses as input. The 3D poses represent the postures and movements of limbs and torso and contain early indicators for the transition between motion types, e.g. wait, start, move, and stop. VRUs often only become visible from the perspective of an approaching vehicle shortly before dangerous situations occur. Therefore, a network architecture is required which is able to forecast trajectories after short time periods and is able to improve the forecasts in case of longer observations. This motivates us to use GRU networks, which are able to use time series of varying duration as inputs, and to investigate the effects of different observation periods on the forecasting results. Our approach is able to make reasonable forecasts even for short observation periods. The use of poses improves the forecasting accuracy, especially for short observation periods compared to a solely head trajectory based approach. Different motion types benefit to different extent from the use of poses and longer observation periods. |
| Databáze: | OpenAIRE |
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