Compressed sensing theory allows for a high-resolution image recovery of sparse image data. However, image scene data from an RGB camera, captured at night-time or in fog, dust, or rainy conditions, is difficult to read. The fusion of IR camera data with RGB camera data allows us to determine the difference between objects in the scene and noise (dust, rain, fog). This paper demonstrates a compressive sensing methodology applied to the scattered image data of the RGB camera to determine objects or obstacles in a scene, which allows for low-cost solutions for the problem of autonomous driving in unfair imaging conditions.
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.
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