Shi et al., 2019 - Google Patents
A novel model based on deep learning for Pedestrian detection and Trajectory predictionShi et al., 2019
View PDF- Document ID
- 11961219692154685389
- Author
- Shi K
- Zhu Y
- Pan H
- Publication year
- Publication venue
- 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
External Links
Snippet
Pedestrian trajectory prediction is faced with the difficulty of data set acquisition, and the traditional models considers a single pedestrian in isolation and ignores the influence of the target pedestrian's neighborhood. This paper presents a pedestrian prediction model that …
- 238000001514 detection method 0 title abstract description 58
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
- G06K9/6217—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
- G06K9/6267—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
- G06K9/68—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
- G06K9/6296—Graphical models, e.g. Bayesian networks
-
- 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
- G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
-
- 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
- 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
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
Similar Documents
Publication | Publication Date | Title |
---|---|---|
He et al. | Obstacle detection of rail transit based on deep learning | |
CN110837778A (en) | Traffic police command gesture recognition method based on skeleton joint point sequence | |
CN110781262B (en) | Semantic map construction method based on visual SLAM | |
CN114970321A (en) | Scene flow digital twinning method and system based on dynamic trajectory flow | |
CN110781838A (en) | Multi-modal trajectory prediction method for pedestrian in complex scene | |
CN103310466B (en) | A kind of monotrack method and implement device thereof | |
CN114802296A (en) | Vehicle track prediction method based on dynamic interaction graph convolution | |
CN108986453A (en) | A kind of traffic movement prediction method based on contextual information, system and device | |
Luo et al. | Unsupervised scene adaptation for semantic segmentation of urban mobile laser scanning point clouds | |
Jiang et al. | Hierarchical semantic segmentation of urban scene point clouds via group proposal and graph attention network | |
CN115829171B (en) | Pedestrian track prediction method combining space-time information and social interaction characteristics | |
CN113129336A (en) | End-to-end multi-vehicle tracking method, system and computer readable medium | |
CN112347838A (en) | Road map fusion | |
Bisagno et al. | Embedding group and obstacle information in lstm networks for human trajectory prediction in crowded scenes | |
Geng et al. | Dynamic-learning spatial-temporal Transformer network for vehicular trajectory prediction at urban intersections | |
Lv et al. | Lane marking regression from confidence area detection to field inference | |
Shi et al. | A novel model based on deep learning for Pedestrian detection and Trajectory prediction | |
Huang et al. | Learning Pedestrian Actions to Ensure Safe Autonomous Driving | |
Song et al. | Vision-based parking space detection: A mask R-CNN approach | |
Kajabad et al. | YOLOv4 for urban object detection: Case of electronic inventory in St. Petersburg | |
CN118279320A (en) | Target instance segmentation model building method based on automatic prompt learning and application thereof | |
CN115294176B (en) | Double-light multi-model long-time target tracking method and system and storage medium | |
Wu et al. | Space-time tree search for long-term trajectory prediction | |
CN113628107A (en) | Face image super-resolution method and system | |
Sohn | AI-Based Transportation Planning and Operation |