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1. GB2560625 - Detecting vehicles in low light conditions

Office
United Kingdom
Application Number 201801029
Application Date 22.01.2018
Publication Number 2560625
Publication Date 19.09.2018
Publication Kind A
IPC
G06K 9/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
CPC
G06K 9/00825
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
00791Recognising scenes perceived from the perspective of a land vehicle, e.g. recognising lanes, obstacles or traffic signs on road scenes
00825Recognition of vehicle or traffic lights
G06K 2209/23
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
2209Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
23Detecting or categorising vehicles
G01S 17/86
GPHYSICS
01MEASURING; TESTING
SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
17Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
86Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
G01S 17/931
GPHYSICS
01MEASURING; TESTING
SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
17Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
88Lidar systems specially adapted for specific applications
93for anti-collision purposes
931of land vehicles
G06K 9/6273
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6267Classification techniques
6268relating to the classification paradigm, e.g. parametric or non-parametric approaches
627based on distances between the pattern to be recognised and training or reference patterns
6271based on distances to prototypes
6272based on distances to cluster centroïds
6273Smoothing the distance, e.g. Radial Basis Function Networks
H04N 7/183
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
NPICTORIAL COMMUNICATION, e.g. TELEVISION
7Television systems
18Closed circuit television systems, i.e. systems in which the signal is not broadcast
183for receiving images from a single remote source
Applicants FORD GLOBAL TECH LLC
Inventors MARYAM MOOSAEI
GUY HOTSON
VIDYA NARIYAMBUT MURALI
MADELINE J GOH
Priority Data 15415733 25.01.2017 US
Title
(EN) Detecting vehicles in low light conditions
Abstract
(EN)
Methods, systems, and computer program products for detecting vehicles 221a 221c in low light conditions. Cameras 204 are used to obtain red, green, blue (RGB) images of the environment around a vehicle. RGB images are converted 213 to LAB images 233. The "A" channel is filtered to extract contours from LAB images 214. The contours are filtered based on their shapes/sizes to reduce false positives from contours unlikely to correspond to vehicles. A neural network 217 classifies an object as a vehicle or non-vehicle based the contours 237. Vehicles can be detected at night as well as in other low light conditions using their head lights and tail (rear) lights, enabling autonomous vehicles to better detect other vehicles in their environment. Vehicle detections can be facilitated using a combination of virtual data, deep learning, and computer vision. The contour may be sent along with range data from a LIDAR sensor 206 to the neural network.