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

Stäcker et al., 2022 - Google Patents

Fusion point pruning for optimized 2d object detection with radar-camera fusion

Stäcker et al., 2022

View PDF
Document ID
4951230904484700178
Author
Stäcker L
Heidenreich P
Rambach J
Stricker D
Publication year
Publication venue
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

External Links

Snippet

Object detection is one of the most important perception tasks for advanced driver assistant systems and autonomous driving. Due to its complementary features and moderate cost, radar-camera fusion is of particular interest in the automotive industry but comes with the …
Continue reading at openaccess.thecvf.com (PDF) (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
    • G06K9/62—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
    • G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
    • G06K9/00369—Recognition of whole body, e.g. static pedestrian or occupant recognition
    • 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
    • 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
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture

Similar Documents

Publication Publication Date Title
Stäcker et al. Fusion point pruning for optimized 2d object detection with radar-camera fusion
Li et al. Improved point-voxel region convolutional neural network: 3D object detectors for autonomous driving
CN111160379A (en) Training method and device of image detection model and target detection method and device
KR102095842B1 (en) Apparatus for Building Grid Map and Method there of
CN107315998B (en) Vehicle class division method and system based on lane line
US20210232871A1 (en) Object detection using multiple sensors and reduced complexity neural networks
CN110879994A (en) Three-dimensional visual inspection detection method, system and device based on shape attention mechanism
Tilly et al. Detection and tracking on automotive radar data with deep learning
CN110569782A (en) Target detection method based on deep learning
CN112651995A (en) On-line multi-target tracking method based on multifunctional aggregation and tracking simulation training
CN114519853B (en) Three-dimensional target detection method and system based on multi-mode fusion
Gasperini et al. CertainNet: Sampling-free uncertainty estimation for object detection
Deng et al. Enet-CRF-LiDAR: LiDAR and camera fusion for multi-scale object recognition
Khamsehashari et al. Improving deep multi-modal 3D object detection for autonomous driving
Hu et al. Embedding CNN-based fast obstacles detection for autonomous vehicles
CN116310368A (en) Laser radar 3D target detection method
Ming et al. Occfusion: A straightforward and effective multi-sensor fusion framework for 3d occupancy prediction
Gomez-Donoso et al. Three-dimensional reconstruction using SFM for actual pedestrian classification
Zhao et al. An Improved Method for Infrared Vehicle and Pedestrian Detection Based on YOLOv5s
CN116310552A (en) Three-dimensional target detection method based on multi-scale feature fusion
Oliveira et al. Multimodal PointPillars for Efficient Object Detection in Autonomous Vehicles
CN116433712A (en) Fusion tracking method and device based on pre-fusion of multi-sensor time sequence sensing results
CN114170267A (en) Target tracking method, device, equipment and computer readable storage medium
Guo et al. Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image
Deng et al. Robust 3d object detection from lidar-radar point clouds via cross-modal feature augmentation