The
article is dedicated to the presentation of a vision-based system for road
vehicle counting and classification.
The
system is able to achieve counting with a very good accuracy even in difficult
scenarios linked to occlusions and/or presence of shadows.
The
principle of the system is to use already installed cameras in road networks
without any additional calibration procedure.
We
propose a robust segmentation algorithm that detects foreground pixels
corresponding to moving vehicles.
First,
the approach models each pixel of the background with an adaptive Gaussian
distribution.
This
model is coupled with a motion detection procedure, which allows correctly
location of moving vehicles in space and time.
The
nature of trials carried out, including peak periods and various vehicle types,
leads to an increase of occlusions between cars and between cars and trucks.
A
specific method for severe occlusion detection, based on the notion of
solidity, has been carried out and tested.
Furthermore,
the method developed in this work is capable of managing shadows with high
resolution.
The
related algorithm has been tested and compared to a classical method.
Experimental
results based on four large datasets show that our method can count and
classify vehicles in real time with a high level of performance (>98%) under
different environmental situations, thus performing better than the
conventional inductive loop detectors.
| Title: | Automatic vehicle counting system for traffic monitoring |
| Authors: | Crouzil, Alain Khoudour, Louahdi Valiere, Paul |
| Keywords: | Computer vision Traffic information systems Traffic image analysis Tracking |
| Issue Date: | 2016 |
| Publisher: | H. : ĐHQGHN |
| Citation: | ISIKNOWLEDGE |
| Abstract: | The article is dedicated to the presentation of a vision-based system for road vehicle counting and classification. The system is able to achieve counting with a very good accuracy even in difficult scenarios linked to occlusions and/or presence of shadows. The principle of the system is to use already installed cameras in road networks without any additional calibration procedure. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. First, the approach models each pixel of the background with an adaptive Gaussian distribution. This model is coupled with a motion detection procedure, which allows correctly location of moving vehicles in space and time. The nature of trials carried out, including peak periods and various vehicle types, leads to an increase of occlusions between cars and between cars and trucks. A specific method for severe occlusion detection, based on the notion of solidity, has been carried out and tested. Furthermore, the method developed in this work is capable of managing shadows with high resolution. The related algorithm has been tested and compared to a classical method. Experimental results based on four large datasets show that our method can count and classify vehicles in real time with a high level of performance (>98%) under different environmental situations, thus performing better than the conventional inductive loop detectors. |
| Description: | JOURNAL OF ELECTRONIC IMAGING Volume: 25 Issue: 5 ; 13 p. ; TNS06376 |
| URI: | http://repository.vnu.edu.vn/handle/VNU_123/26708 |
| Appears in Collections: | Bài báo của ĐHQGHN trong Web of Science |
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