[go: up one dir, main page]

Wang et al., 2024 - Google Patents

Research, applications and prospects of event-based pedestrian detection: a survey

Wang et al., 2024

View PDF
Document ID
742988467369426350
Author
Wang H
Nie Y
Li Y
Liu H
Liu M
Cheng W
Wang Y
Publication year
Publication venue
arXiv preprint arXiv:2407.04277

External Links

Snippet

Event-based cameras, inspired by the biological retina, have evolved into cutting-edge sensors distinguished by their minimal power requirements, negligible latency, superior temporal resolution, and expansive dynamic range. At present, cameras used for pedestrian …
Continue reading at arxiv.org (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
    • G06K9/62—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
    • G06K9/00624—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
    • 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
    • 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
    • G06K9/36—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
    • G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • 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
    • 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
    • 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
Jana et al. YOLO based Detection and Classification of Objects in video records
Chen et al. An improved Yolov3 based on dual path network for cherry tomatoes detection
CN111291699A (en) Substation personnel behavior identification method based on monitoring video time sequence action positioning and abnormity detection
Almahadin et al. Enhancing video anomaly detection using spatio-temporal autoencoders and convolutional lstm networks
Roka et al. Anomaly behavior detection analysis in video surveillance: a critical review
JP7660264B2 (en) OBJECT DETECTION DEVICE, OBJECT DETECTION METHOD, AND OBJECT DETECTION SYSTEM
Zhang et al. A universal event-based plug-in module for visual object tracking in degraded conditions
Yin Object Detection Based on Deep Learning: A Brief Review
Wang et al. Research, applications and prospects of event-based pedestrian detection: a survey
Yuan et al. Learning bottleneck transformer for event image-voxel feature fusion based classification
Tran et al. Advancing violence detection with graph-based skeleton motion analysis
Bondalapati et al. RETRACTED ARTICLE: Moving object detection based on unified model
Zhang et al. A comprehensive review of deep learning approaches for group activity analysis
Farhat et al. Optimized deep learning for pedestrian safety in autonomous vehicles
Li et al. Pedestrian motion path detection method based on deep learning and foreground detection
Zhu et al. Improved RAD-YOLOv8s deep learning algorithm for personnel detection in deep mining workings of mines
Chen et al. Automatic detection of foreign object intrusion along railway tracks based on MACENet
Wang et al. Event-Based Vision Sensing and Its Application to Pedestrian Detection for Autonomous Driving
Giriprasad et al. Anomalies detection from video surveillance using support vector trained deep neural network classifier
Siddiqui et al. IoT based human activity recognition using deep learning
Kwak et al. Detection of Abnormal Human Behavior Using Unsupervised Learning in Video Surveillance Systems
Lubis et al. Towards efficient crowd counting and behavior analysis using YOLOv11
Rajakumar et al. Extreme-YOLOv5: Human Activity Recognition of Group Pedestrians Under Different Weather Conditions
Li et al. A Review of Multi-modal Human Motion Recognition Based on Deep Learning
Houssein et al. Deep learning techniques for video-based violence detection: a comprehensive review