Details
| Original language | English |
|---|---|
| Pages (from-to) | 683-688 |
| Number of pages | 6 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 43 |
| Issue number | B2 |
| Publication status | Published - 12 Aug 2020 |
| Event | 2020 24th ISPRS Congress - Technical Commission II - Nice, Virtual, France Duration: 31 Aug 2020 → 2 Sept 2020 |
Abstract
Estimating vehicle poses is crucial for generating precise movement trajectories from (surveillance) camera data. Additionally for real time applications this task has to be solved in an efficient way. In this paper we introduce a deep convolutional neural network for pose estimation of vehicles from image patches. For a given 2D image patch our approach estimates the 2D coordinates of the image representing the exact center ground point (cx, cy) and the orientation of the vehicle - represented by the elevation angle (e) of the camera with respect to the vehicle's center ground point and the azimuth rotation (a) of the vehicle with respect to the camera. To train a accurate model a large and diverse training dataset is needed. Collecting and labeling such large amount of data is very time consuming and expensive. Due to the lack of a sufficient amount of training data we show furthermore, that also rendered 3D vehicle models with artificial generated textures are nearly adequate for training.
Keywords
- Deep Learning, Pose Estimation, Surveillance Video Analysis, Trajectory Analysis, Trajectory Extraction
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 43, No. B2, 12.08.2020, p. 683-688.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Learning the 3D Pose of Vehicles from 2D Vehicle Patches
AU - Koetsier, C.
AU - Peters, T.
AU - Sester, M.
PY - 2020/8/12
Y1 - 2020/8/12
N2 - Estimating vehicle poses is crucial for generating precise movement trajectories from (surveillance) camera data. Additionally for real time applications this task has to be solved in an efficient way. In this paper we introduce a deep convolutional neural network for pose estimation of vehicles from image patches. For a given 2D image patch our approach estimates the 2D coordinates of the image representing the exact center ground point (cx, cy) and the orientation of the vehicle - represented by the elevation angle (e) of the camera with respect to the vehicle's center ground point and the azimuth rotation (a) of the vehicle with respect to the camera. To train a accurate model a large and diverse training dataset is needed. Collecting and labeling such large amount of data is very time consuming and expensive. Due to the lack of a sufficient amount of training data we show furthermore, that also rendered 3D vehicle models with artificial generated textures are nearly adequate for training.
AB - Estimating vehicle poses is crucial for generating precise movement trajectories from (surveillance) camera data. Additionally for real time applications this task has to be solved in an efficient way. In this paper we introduce a deep convolutional neural network for pose estimation of vehicles from image patches. For a given 2D image patch our approach estimates the 2D coordinates of the image representing the exact center ground point (cx, cy) and the orientation of the vehicle - represented by the elevation angle (e) of the camera with respect to the vehicle's center ground point and the azimuth rotation (a) of the vehicle with respect to the camera. To train a accurate model a large and diverse training dataset is needed. Collecting and labeling such large amount of data is very time consuming and expensive. Due to the lack of a sufficient amount of training data we show furthermore, that also rendered 3D vehicle models with artificial generated textures are nearly adequate for training.
KW - Deep Learning
KW - Pose Estimation
KW - Surveillance Video Analysis
KW - Trajectory Analysis
KW - Trajectory Extraction
UR - http://www.scopus.com/inward/record.url?scp=85091098644&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLIII-B2-2020-683-2020
DO - 10.5194/isprs-archives-XLIII-B2-2020-683-2020
M3 - Conference article
AN - SCOPUS:85091098644
VL - 43
SP - 683
EP - 688
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - B2
T2 - 2020 24th ISPRS Congress - Technical Commission II
Y2 - 31 August 2020 through 2 September 2020
ER -