TY - GEN
T1 - Vision-based blind spot warning system by deep neural networks
AU - Virgilio G, Víctor R.
AU - Sossa, Humberto
AU - Zamora, Erik
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Traffic accidents represent one of the most serious problems around the world. Many efforts have been concentrated on implementing Advanced Driver Assistance Systems (ADAS) to increase safety by reducing critical tasks faced by the driver. In this paper, a Blind Spot Warning (BSW) system capable of virtualizing cars around the driver’s vehicle is presented. The system is based on deep neural models for car detection and depth estimation using images captured with a camera located on top of the main vehicle, then transformations are applied to the image and to generate the appropriate information format. Finally the cars in the environment are represented in a 3D graphical interface. We present a comparison between car detectors and another one between depth estimators from which we choose the best performance ones to be implemented in the BSW system. In particular, our system offers a more intuitive assistance interface for the driver allowing a better and quicker understanding of the environment from monocular cameras.
AB - Traffic accidents represent one of the most serious problems around the world. Many efforts have been concentrated on implementing Advanced Driver Assistance Systems (ADAS) to increase safety by reducing critical tasks faced by the driver. In this paper, a Blind Spot Warning (BSW) system capable of virtualizing cars around the driver’s vehicle is presented. The system is based on deep neural models for car detection and depth estimation using images captured with a camera located on top of the main vehicle, then transformations are applied to the image and to generate the appropriate information format. Finally the cars in the environment are represented in a 3D graphical interface. We present a comparison between car detectors and another one between depth estimators from which we choose the best performance ones to be implemented in the BSW system. In particular, our system offers a more intuitive assistance interface for the driver allowing a better and quicker understanding of the environment from monocular cameras.
KW - ADAS (advanced driver-assistance systems)
KW - BSW (blind spots warning)
KW - Neural networks
KW - Object detection
KW - SIDE (single image depth estimation)
UR - http://www.scopus.com/inward/record.url?scp=85087283458&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-49076-8_18
DO - 10.1007/978-3-030-49076-8_18
M3 - Contribución a la conferencia
AN - SCOPUS:85087283458
SN - 9783030490751
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 185
EP - 194
BT - Pattern Recognition - 12th Mexican Conference, MCPR 2020, Proceedings
A2 - Figueroa Mora, Karina Mariela
A2 - Anzurez Marín, Juan
A2 - Cerda, Jaime
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Martínez-Trinidad, José Francisco
A2 - Olvera-López, José Arturo
PB - Springer
T2 - 12th Mexican Conference on Pattern Recognition, MCPR 2020
Y2 - 24 June 2020 through 27 June 2020
ER -
