Li et al., 2017 - Google Patents
Traffic scene segmentation based on RGB-D image and deep learningLi et al., 2017
- Document ID
- 4739809139158410574
- Author
- Li L
- Qian B
- Lian J
- Zheng W
- Zhou Y
- Publication year
- Publication venue
- IEEE Transactions on Intelligent Transportation Systems
External Links
Snippet
Semantic segmentation of traffic scenes has potential applications in intelligent transportation systems. Deep learning techniques can improve segmentation accuracy, especially when the information from depth maps is introduced. However, little research has …
- 230000011218 segmentation 0 title abstract description 64
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
- G06K9/62—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
- G06K9/36—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
- G06K9/62—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
- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
-
- 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
- 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
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
- G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- 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
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | Traffic scene segmentation based on RGB-D image and deep learning | |
Pang et al. | Hierarchical dynamic filtering network for RGB-D salient object detection | |
Zhou et al. | Contextual ensemble network for semantic segmentation | |
Yang et al. | Multifeature fusion-based object detection for intelligent transportation systems | |
Zhang et al. | Efficient inductive vision transformer for oriented object detection in remote sensing imagery | |
Yang et al. | Small object augmentation of urban scenes for real-time semantic segmentation | |
Zhou et al. | Enhanced SSD with interactive multi-scale attention features for object detection | |
Di et al. | Cross-domain traffic scene understanding: A dense correspondence-based transfer learning approach | |
Zhou et al. | Enhance the recognition ability to occlusions and small objects with Robust Faster R-CNN | |
An et al. | Object recognition algorithm based on optimized nonlinear activation function-global convolutional neural network | |
Lu et al. | Generalized haar filter-based object detection for car sharing services | |
Tsai et al. | A single‐stage face detection and face recognition deep neural network based on feature pyramid and triplet loss | |
Chilukuri et al. | A robust object detection system with occlusion handling for mobile devices | |
Du et al. | Improved detection method for traffic signs in real scenes applied in intelligent and connected vehicles | |
Huang et al. | Pedestrian detection using RetinaNet with multi-branch structure and double pooling attention mechanism | |
Ren et al. | A new multi-scale pedestrian detection algorithm in traffic environment | |
Yang et al. | Fine-grained lip image segmentation using fuzzy logic and graph reasoning | |
Wang et al. | Semantic annotation for complex video street views based on 2D–3D multi-feature fusion and aggregated boosting decision forests | |
An et al. | Road traffic sign recognition algorithm based on cascade attention-modulation fusion mechanism | |
Wang et al. | Calyolov4: lightweight yolov4 target detection based on coordinated attention | |
Fadavi Amiri et al. | Improving image segmentation using artificial neural networks and evolutionary algorithms | |
Feng et al. | A multibranch object detection method for traffic scenes | |
Wang | Motion recognition based on deep learning and human joint points | |
Choudhury et al. | Employing generative adversarial network in low-light animal detection | |
Tong et al. | Few-shot object detection via understanding convolution and attention |