Method

Fast multi-task CNN with orientation regression [at] [multi-task CNN]


Submitted on 18 Oct. 2018 17:25 by
Malte Oeljeklaus (TU Dortmund)

Running time:25.1 ms
Environment:GPU @ 2.0 Ghz (Python)

Method Description:
A convolutional neural network (CNN) comprised of a shared encoder stage and specific decoders for road segmentation and object detection. the detection stage is extended to predict the orientation of detected objects. The orientation estimate guides a consecutive 3D bounding box estimation based on analytic geometry.
Parameters:
Latex Bibtex:
@inproceedings{Oeljeklaus18,
title={A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes},
author={Oeljeklaus, Malte and Hoffmann, Frank and Bertram, Torsten},
booktitle={IEEE Intelligent Transportation Systems Conference},
year={2018}
}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 83.45 % 75.21 % 66.89 %
Car (Orientation) 76.69 % 66.19 % 58.11 %
Car (Bird's Eye View) 0.00 % 0.00 % 0.00 %
Pedestrian (Detection) 51.38 % 40.34 % 34.98 %
Pedestrian (Orientation) 33.58 % 26.98 % 23.07 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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