[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Xia et al., 2019 - Google Patents

Abnormal event detection method in surveillance video based on temporal CNN and sparse optical flow

Xia et al., 2019

Document ID
14985192419614820730
Author
Xia H
Li T
Liu W
Zhong X
Yuan J
Publication year
Publication venue
Proceedings of the 2019 5th International Conference on Computing and Data Engineering

External Links

Snippet

Abnormal event detection in surveillance video is very important for intelligent analysis of video. Most anomalous event detection methods rely on complex feature extraction to represent motion and appearance. Convolutional Neural Network (CNN) is a powerful and …
Continue reading at dl.acm.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30781Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784Information 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
    • G06F17/30799Information 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00711Recognising video content, e.g. extracting audiovisual features from movies, extracting representative key-frames, discriminating news vs. sport content
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00771Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/40Analysis of texture
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
CN108764085B (en) Crowd counting method based on generation of confrontation network
Bappy et al. Exploiting spatial structure for localizing manipulated image regions
CN106778595B (en) Method for detecting abnormal behaviors in crowd based on Gaussian mixture model
Khan et al. Revisiting crowd counting: State-of-the-art, trends, and future perspectives
CN111723693B (en) Crowd counting method based on small sample learning
CN110717411A (en) Pedestrian re-identification method based on deep layer feature fusion
CN111353395A (en) Face changing video detection method based on long-term and short-term memory network
CN111709311A (en) Pedestrian re-identification method based on multi-scale convolution feature fusion
Halawa et al. Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera
Yang et al. Spatiotemporal trident networks: detection and localization of object removal tampering in video passive forensics
CN113536972B (en) Self-supervision cross-domain crowd counting method based on target domain pseudo label
Gan et al. Video object forgery detection algorithm based on VGG-11 convolutional neural network
CN107659754B (en) Effective concentration method for monitoring video under condition of tree leaf disturbance
Luo et al. Traffic analytics with low-frame-rate videos
CN111738054A (en) Behavior anomaly detection method based on space-time self-encoder network and space-time CNN
Hu et al. Parallel spatial-temporal convolutional neural networks for anomaly detection and location in crowded scenes
Tralic et al. Video frame copy-move forgery detection based on cellular automata and local binary patterns
Tao et al. Smoke vehicle detection based on robust codebook model and robust volume local binary count patterns
Xia et al. Abnormal event detection method in surveillance video based on temporal CNN and sparse optical flow
Oraibi et al. Enhancement digital forensic approach for inter-frame video forgery detection using a deep learning technique
Shivanandappa et al. Extraction of image resampling using correlation aware convolution neural networks for image tampering detection
Zhong et al. A fast forgery frame detection method for video copy-move inter/intra-frame identification
CN116824641B (en) Gesture classification method, device, equipment and computer storage medium
Sreelekshmi et al. Deep forgery detect: enhancing social media security through deep learning-based forgery detection
Li et al. Local co-occurrence selection via partial least squares for pedestrian detection