Nothing Special   »   [go: up one dir, main page]

Rapson et al., 2019 - Google Patents

A performance comparison of deep learning methods for real-time localisation of vehicle lights in video frames

Rapson et al., 2019

Document ID
5593235558502174177
Author
Rapson C
Seet B
Naeem M
Lee J
Klette R
Publication year
Publication venue
2019 IEEE Intelligent Transportation Systems Conference (ITSC)

External Links

Snippet

A vehicle's braking lights can help to infer its future trajectory. Visible light communication using vehicle lights can also transmit other safety information to assist drivers with collision avoidance (whether the drivers be human or autonomous). Both these use cases require …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00791—Recognising scenes perceived from the perspective of a land vehicle, e.g. recognising lanes, obstacles or traffic signs on road scenes
    • G06K9/00825—Recognition of vehicle or traffic lights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00791—Recognising scenes perceived from the perspective of a land vehicle, e.g. recognising lanes, obstacles or traffic signs on road scenes
    • G06K9/00798—Recognition of lanes or road borders, e.g. of lane markings, or recognition of driver's driving pattern in relation to lanes perceived from the vehicle; Analysis of car trajectory relative to detected road
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/20—Image acquisition
    • G06K9/32—Aligning or centering of the image pick-up or image-field
    • G06K9/3233—Determination of region of interest
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/23—Detecting or categorising vehicles

Similar Documents

Publication Publication Date Title
Yang et al. Vehicle detection in intelligent transportation systems and its applications under varying environments: A review
Behrendt et al. A deep learning approach to traffic lights: Detection, tracking, and classification
Barnes et al. Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy
Wang et al. Appearance-based brake-lights recognition using deep learning and vehicle detection
Romdhane et al. An improved traffic signs recognition and tracking method for driver assistance system
Timofte et al. Combining traffic sign detection with 3D tracking towards better driver assistance
US20170206427A1 (en) Efficient, High-Resolution System and Method to Detect Traffic Lights
Prakash et al. Robust obstacle detection for advanced driver assistance systems using distortions of inverse perspective mapping of a monocular camera
Wu et al. A Real‐Time Embedded Blind Spot Safety Assistance System
Vancea et al. Vehicle taillight detection and tracking using deep learning and thresholding for candidate generation
Niknejad et al. Vision-based vehicle detection for nighttime with discriminately trained mixture of weighted deformable part models
Rapson et al. A performance comparison of deep learning methods for real-time localisation of vehicle lights in video frames
Nava et al. A collision warning oriented brake lights detection and classification algorithm based on a mono camera sensor
Balisavira et al. Real-time object detection by road plane segmentation technique for ADAS
Moizumi et al. Traffic light detection considering color saturation using in-vehicle stereo camera
Gopalkrishnan et al. Robust detection, assocation, and localization of vehicle lights: A context-based cascaded CNN approach and evaluations
Niknejad et al. Vehicle detection and tracking at nighttime for urban autonomous driving
Chen et al. Vision-based traffic surveys in urban environments
Płaczek A real time vehicle detection algorithm for vision-based sensors
Chen et al. Robust rear light status recognition using symmetrical surfs
Sri Jamiya et al. A survey on vehicle detection and tracking algorithms in real time video surveillance
Vijverberg et al. High-level traffic-violation detection for embedded traffic analysis
Valiere et al. Robust vehicle counting with severe shadows and occlusions
Wu et al. Robust and low complexity obstacle detection and tracking
Tourani et al. Challenges of video-based vehicle detection and tracking in intelligent transportation systems