The design and development of autonomous vehicles ensure to move safely on roads while focusing on pedestrian detection systems has brought convention so that pedestrians can be detected quickly and precisely. Moreover, the researchers have mentioned that pedestrian skin detection has proven to be a tough challenge since the color of the skin can vary in appearance due to various factors such as weather conditions, sun lighting, occlusion, race, etc. Our proposed methodology, the radar-camera fusion technique, is used to predict the obstacle in any scenario. A convolution neural network extracts pedestrian features from RGB images and radar data. Also, we have introduced data preparation and feature extraction. We feature mapping to get more detection accuracy and clustering to find the similarities between features that will attain darker skin pedestrian details.
Autonomous vehicles design and development can move safely on roads while sensing the environment to focus on pedestrian detection systems so that people can be detected as quickly and accurately as possible. First, however, it is critical to examine the pedestrians themselves and their color, which benefits from being insensitive to changes in scale and partial occlusion. Moreover, human skin detection has proven to be a tough challenge since skin color can vary considerably in appearance due to various factors such as lighting, race, and imaging circumstances.
Unfortunately, human skin detection has not been thoroughly investigated in this circumstance, and it appears that many studies do not address this systematically when it comes to pedestrian detection systems for autonomous cars.
To overcome this issue, we are using a Radar-Camera fusion technique to predict obstacles in various daylight situations.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.