Towards view-invariant vehicle speed detection from driving simulator
images
- URL: http://arxiv.org/abs/2206.00343v1
- Date: Wed, 1 Jun 2022 09:14:45 GMT
- Title: Towards view-invariant vehicle speed detection from driving simulator
images
- Authors: Antonio Hern\'andez Mart\'inez, David Fernandez Llorca, Iv\'an
Garc\'ia Daza
- Abstract summary: We address the question of whether complex 3D-CNN architectures are capable of implicitly learning view-invariant speeds using a single model.
The results are very promising as they show that a single model with data from multiple views reports even better accuracy than camera-specific models.
- Score: 0.31498833540989407
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The use of cameras for vehicle speed measurement is much more cost effective
compared to other technologies such as inductive loops, radar or laser.
However, accurate speed measurement remains a challenge due to the inherent
limitations of cameras to provide accurate range estimates. In addition,
classical vision-based methods are very sensitive to extrinsic calibration
between the camera and the road. In this context, the use of data-driven
approaches appears as an interesting alternative. However, data collection
requires a complex and costly setup to record videos under real traffic
conditions from the camera synchronized with a high-precision speed sensor to
generate the ground truth speed values. It has recently been demonstrated that
the use of driving simulators (e.g., CARLA) can serve as a robust alternative
for generating large synthetic datasets to enable the application of deep
learning techniques for vehicle speed estimation for a single camera. In this
paper, we study the same problem using multiple cameras in different virtual
locations and with different extrinsic parameters. We address the question of
whether complex 3D-CNN architectures are capable of implicitly learning
view-invariant speeds using a single model, or whether view-specific models are
more appropriate. The results are very promising as they show that a single
model with data from multiple views reports even better accuracy than
camera-specific models, paving the way towards a view-invariant vehicle speed
measurement system.
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