TY - GEN
T1 - Car parking assistance based on time-or-flight camera
AU - Pelaez, Luis Paarup
AU - Recalde, Myriam E.Vaca
AU - Munoz, Enrique D.Marti
AU - Larrauri, Jesus Murgoitio
AU - Rastelli, Joshue M.Perez
AU - Druml, Norbert
AU - Hillbrand, Bernhard
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - External sensing for automative applications are key tools for the development of Advanced Driver Assistance Systems (ADAS), since they can sense and analyse the environment around the vehicle by providing pictures of the scene behind the vehicle. Parking assistance systems are already available in the market. However, most of these applications are based on ultrasonic sensors, wide-angle image cameras, RADAR, etc, which present some drawbacks such as dependency to light conditions or high maintenance cost, among others. This paper proposes an approach for assisting drivers to park through the processing of data derived by a 3D Time-of-Flight (ToF) camera and the reconstruction of the objects identified around the vehicle. The proposed technique is focused on fusion of two parallel processing technologies, a visual one through the intensity image and a spatial one through the point cloud. Both of them are centered on the detection of the vehicle's plate to estimate its position and determine free spots in the parking. This novel methodology improves the detection of surrounding elements, since it helps to solve two main problems with this kind of devices: 1) the degraded performance under bright ambient light problem (occurring mainly in outdoor parkings), that causes shadows and brightness in the images, hindering its process to detect objects; and 2) the limited detection of low reflection objects such as dark cars. Moreover, this fusion allows to link each pixel of the image with a 3D position, and vice versa, giving the point cloud a visual reference. The system is evaluated through a Renault Twizy platform in real conditions.
AB - External sensing for automative applications are key tools for the development of Advanced Driver Assistance Systems (ADAS), since they can sense and analyse the environment around the vehicle by providing pictures of the scene behind the vehicle. Parking assistance systems are already available in the market. However, most of these applications are based on ultrasonic sensors, wide-angle image cameras, RADAR, etc, which present some drawbacks such as dependency to light conditions or high maintenance cost, among others. This paper proposes an approach for assisting drivers to park through the processing of data derived by a 3D Time-of-Flight (ToF) camera and the reconstruction of the objects identified around the vehicle. The proposed technique is focused on fusion of two parallel processing technologies, a visual one through the intensity image and a spatial one through the point cloud. Both of them are centered on the detection of the vehicle's plate to estimate its position and determine free spots in the parking. This novel methodology improves the detection of surrounding elements, since it helps to solve two main problems with this kind of devices: 1) the degraded performance under bright ambient light problem (occurring mainly in outdoor parkings), that causes shadows and brightness in the images, hindering its process to detect objects; and 2) the limited detection of low reflection objects such as dark cars. Moreover, this fusion allows to link each pixel of the image with a 3D position, and vice versa, giving the point cloud a visual reference. The system is evaluated through a Renault Twizy platform in real conditions.
UR - http://www.scopus.com/inward/record.url?scp=85072286129&partnerID=8YFLogxK
U2 - 10.1109/IVS.2019.8813820
DO - 10.1109/IVS.2019.8813820
M3 - Conference contribution
AN - SCOPUS:85072286129
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1753
EP - 1759
BT - 2019 IEEE Intelligent Vehicles Symposium, IV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE Intelligent Vehicles Symposium, IV 2019
Y2 - 9 June 2019 through 12 June 2019
ER -