Planet

Title: The CarSpeed project
Speaker: Victor Hidalgo
Date: 2008/09/30, 19h
Place: Universidad Rey Juan Carlos at Robotics Lab (Departamental-II)
Next tuesday, September 30 at 19h, we will hold the seminar “The CarSpeed project’ in the Robotics Lab. Victor Hidalgo will explain his work in that project, and the underlying computer vision technology. CarSpeed deals with an application that it is able to carry out precise estimation of vehicle speeds. It also counts the number of the vehicles that travel through the road.
The system uses a single off-the-shelf camera. Its images are carefully analyzed to give estimations of vehicles speed. The application detects and tracks the vehicles on the road. The detection step allows the system to realize the appearance of new vehicles. It uses a sampled motion filter. The tracking algorithm starts for each new detected vehicle. It implements an homography and an evolutionary algorithm that keep several speed hypothesis while they are compatible with the camera images. Check out this video (right) for a demo.
You are all kindly invited!

Title: The CarSpeed project
Speaker: Victor Hidalgo
Date: 2008/09/30, 19h
Place: Universidad Rey Juan Carlos at Robotics Lab (Departamental-II)
Next tuesday, September 30 at 19h, we will hold the seminar “The CarSpeed project’ in the Robotics Lab. Victor Hidalgo will explain his work in that project, and the underlying computer vision technology. CarSpeed deals with an application that it is able to carry out precise estimation of vehicle speeds. It also counts the number of the vehicles that travel through the road.
The system uses a single off-the-shelf camera. Its images are carefully analyzed to give estimations of vehicles speed. The application detects and tracks the vehicles on the road. The detection step allows the system to realize the appearance of new vehicles. It uses a sampled motion filter. The tracking algorithm starts for each new detected vehicle. It implements an homography and an evolutionary algorithm that keep several speed hypothesis while they are compatible with the camera images. Check out this video (right) for a demo.
You are all kindly invited!

Title: The CarSpeed project
Speaker: Victor Hidalgo
Date: 2008/09/30, 19h
Place: Universidad Rey Juan Carlos at Robotics Lab (Departamental-II)
Next tuesday, September 30 at 19h, we will hold the seminar “The CarSpeed project’ in the Robotics Lab. Victor Hidalgo will explain his work in that project, and the underlying computer vision technology. CarSpeed deals with an application that it is able to carry out precise estimation of vehicle speeds. It also counts the number of the vehicles that travel through the road.
The system uses a single off-the-shelf camera. Its images are carefully analyzed to give estimations of vehicles speed. The application detects and tracks the vehicles on the road. The detection step allows the system to realize the appearance of new vehicles. It uses a sampled motion filter. The tracking algorithm starts for each new detected vehicle. It implements an homography and an evolutionary algorithm that keep several speed hypothesis while they are compatible with the camera images. Check out this video (right) for a demo.
You are all kindly invited!

Title: The CarSpeed project
Speaker: Victor Hidalgo
Date: 2008/09/30, 19h
Place: Universidad Rey Juan Carlos at Robotics Lab (Departamental-II)
Next tuesday, September 30 at 19h, we will hold the seminar “The CarSpeed project’ in the Robotics Lab. Victor Hidalgo will explain his work in that project, and the underlying computer vision technology. CarSpeed deals with an application that it is able to carry out precise estimation of vehicle speeds. It also counts the number of the vehicles that travel through the road.
The system uses a single off-the-shelf camera. Its images are carefully analyzed to give estimations of vehicles speed. The application detects and tracks the vehicles on the road. The detection step allows the system to realize the appearance of new vehicles. It uses a sampled motion filter. The tracking algorithm starts for each new detected vehicle. It implements an homography and an evolutionary algorithm that keep several speed hypothesis while they are compatible with the camera images. Check out this video (right) for a demo.
You are all kindly invited!

Title: The CarSpeed project
Speaker: Victor Hidalgo
Date: 2008/09/30, 19h
Place: Universidad Rey Juan Carlos at Robotics Lab (Departamental-II)
Next tuesday, September 30 at 19h, we will hold the seminar “The CarSpeed project’ in the Robotics Lab. Victor Hidalgo will explain his work in that project, and the underlying computer vision technology. CarSpeed deals with an application that it is able to carry out precise estimation of vehicle speeds. It also counts the number of the vehicles that travel through the road.
The system uses a single off-the-shelf camera. Its images are carefully analyzed to give estimations of vehicles speed. The application detects and tracks the vehicles on the road. The detection step allows the system to realize the appearance of new vehicles. It uses a sampled motion filter. The tracking algorithm starts for each new detected vehicle. It implements an homography and an evolutionary algorithm that keep several speed hypothesis while they are compatible with the camera images. Check out this video (right) for a demo.
You are all kindly invited!

I’ve finished my project. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, like Departamental II of this university. We’ve solved the following problems:
- Navigation in dynamic environments. Public places are often packed with people. People behave not necessarily cooperatively. Our approach provides means for safe and effective navigation through crowds.
- Navigation in unmodified environments. No modification of the environment is necessary for the robot’s operation.
- Localization. In every operation, our robot continuously tracks its position using its maps. Position estimates are necessary for the robot to know where to move when navigating to a specific goal, and to ensure the robot does not accidentally leave its operational area.
To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem.
So, in this video we’ve used Monte Carlo localization method where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
Link for more information: http://jde.gsyc.es/index.php/jmvega_guide_robot

I’ve finished my project. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, like Departamental II of this university. We’ve solved the following problems:
- Navigation in dynamic environments. Public places are often packed with people. People behave not necessarily cooperatively. Our approach provides means for safe and effective navigation through crowds.
- Navigation in unmodified environments. No modification of the environment is necessary for the robot’s operation.
- Localization. In every operation, our robot continuously tracks its position using its maps. Position estimates are necessary for the robot to know where to move when navigating to a specific goal, and to ensure the robot does not accidentally leave its operational area.
To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem.
So, in this video we’ve used Monte Carlo localization method where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
Link for more information: http://jde.gsyc.es/index.php/jmvega_guide_robot

I’ve finished my project. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, like Departamental II of this university. We’ve solved the following problems:
- Navigation in dynamic environments. Public places are often packed with people. People behave not necessarily cooperatively. Our approach provides means for safe and effective navigation through crowds.
- Navigation in unmodified environments. No modification of the environment is necessary for the robot’s operation.
- Localization. In every operation, our robot continuously tracks its position using its maps. Position estimates are necessary for the robot to know where to move when navigating to a specific goal, and to ensure the robot does not accidentally leave its operational area.
To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem.
So, in this video we’ve used Monte Carlo localization method where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
Link for more information: http://jde.gsyc.es/index.php/jmvega_guide_robot

I’ve finished my project. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, like Departamental II of this university. We’ve solved the following problems:
- Navigation in dynamic environments. Public places are often packed with people. People behave not necessarily cooperatively. Our approach provides means for safe and effective navigation through crowds.
- Navigation in unmodified environments. No modification of the environment is necessary for the robot’s operation.
- Localization. In every operation, our robot continuously tracks its position using its maps. Position estimates are necessary for the robot to know where to move when navigating to a specific goal, and to ensure the robot does not accidentally leave its operational area.
To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem.
So, in this video we’ve used Monte Carlo localization method where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
Link for more information: http://jde.gsyc.es/index.php/jmvega_guide_robot

I’ve finished my project. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, like Departamental II of this university. We’ve solved the following problems:
- Navigation in dynamic environments. Public places are often packed with people. People behave not necessarily cooperatively. Our approach provides means for safe and effective navigation through crowds.
- Navigation in unmodified environments. No modification of the environment is necessary for the robot’s operation.
- Localization. In every operation, our robot continuously tracks its position using its maps. Position estimates are necessary for the robot to know where to move when navigating to a specific goal, and to ensure the robot does not accidentally leave its operational area.
To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem.
So, in this video we’ve used Monte Carlo localization method where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
Link for more information: http://jde.gsyc.es/index.php/jmvega_guide_robot