Chen et al., 2020 - Google Patents
Vehicle detection based on multifeature extraction and recognition adopting RBF neural network on ADAS systemChen et al., 2020
View PDF- Document ID
- 8750451043162310195
- Author
- Chen X
- Chen H
- Xu H
- Publication year
- Publication venue
- Complexity
External Links
Snippet
A region of interest (ROI) that may contain vehicles is extracted based on the composite features on vehicle's bottom shadow and taillights by setting a gray threshold on vehicle shadow region and a series of constraints on taillights. In order to identify the existence of …
- 238000001514 detection method 0 title abstract description 65
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
- G06K9/00624—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
- G06K9/00818—Recognising traffic signs
-
- 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
- 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
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- 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
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ye et al. | Lane detection method based on lane structural analysis and CNNs | |
CN111310583B (en) | Vehicle abnormal behavior identification method based on improved long-term and short-term memory network | |
Chen et al. | Vehicle detection based on multifeature extraction and recognition adopting RBF neural network on ADAS system | |
Şentaş et al. | Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification | |
Khalid et al. | Automatic measurement of the traffic sign with digital segmentation and recognition<? show [AQ ID= Q1]?> | |
Chen et al. | An effective approach of vehicle detection using deep learning | |
Wang et al. | Detection and classification of moving vehicle from video using multiple spatio-temporal features | |
Cai et al. | Night‐Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning | |
US20210166085A1 (en) | Object Classification Method, Object Classification Circuit, Motor Vehicle | |
Zhang et al. | A framework for turning behavior classification at intersections using 3D LIDAR | |
Li | Image semantic segmentation method based on GAN network and ENet model | |
Habeeb et al. | Deep‐Learning‐Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition | |
Zhang et al. | An approach focusing on the convolutional layer characteristics of the VGG network for vehicle tracking | |
Guo et al. | Real-time vehicle object detection method based on multi-scale feature fusion | |
Liu et al. | On‐road multi‐vehicle tracking algorithm based on an improved particle filter | |
CN117475142A (en) | Millimeter wave radar point cloud segmentation method based on ViT and oriented to autopilot | |
Song et al. | A robust detection method for multilane lines in complex traffic scenes | |
Dhanakshirur et al. | A framework for lane prediction on unstructured roads | |
Wang et al. | A 64‐Line Lidar‐Based Road Obstacle Sensing Algorithm for Intelligent Vehicles | |
Yang et al. | Intelligent intersection vehicle and pedestrian detection based on convolutional neural network | |
Wang et al. | A Front Water Recognition Method Based on Image Data for Off‐Road Intelligent Vehicle | |
Zhang et al. | The Line Pressure Detection for Autonomous Vehicles Based on Deep Learning | |
Li et al. | Multifeature fusion vehicle detection algorithm based on choquet integral | |
Ji et al. | Automatic Lane Line Detection System Based on Artificial Intelligence | |
Yuan et al. | Vehicle detection based on area and proportion prior with faster-RCNN |