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

Ammar et al., 2019 - Google Patents

Moving objects segmentation based on deepsphere in video surveillance

Ammar et al., 2019

View PDF
Document ID
3870458108206020314
Author
Ammar S
Bouwmans T
Zaghden N
Neji M
Publication year
Publication venue
Advances in Visual Computing: 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part II 14

External Links

Snippet

Segmentation of moving objects from video sequences plays an important role in many computer vision applications. In this paper, we present a background subtraction approach based on deep neural networks. More specifically, we propose to employ and validate an …
Continue reading at www.academia.edu (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
    • 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
    • G06K9/00711—Recognising video content, e.g. extracting audiovisual features from movies, extracting representative key-frames, discriminating news vs. sport 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
    • 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
    • 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
    • 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
    • G06T7/20—Analysis of motion
    • 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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Le et al. Attention-based residual autoencoder for video anomaly detection
Lu et al. Few-shot scene-adaptive anomaly detection
Sreenu et al. Intelligent video surveillance: a review through deep learning techniques for crowd analysis
Cho et al. Unsupervised video anomaly detection via normalizing flows with implicit latent features
Himeur et al. Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization
Sultana et al. Unsupervised deep context prediction for background estimation and foreground segmentation
Wu et al. An explainable and efficient deep learning framework for video anomaly detection
Ammar et al. Moving objects segmentation based on deepsphere in video surveillance
Asad et al. Anomaly3D: Video anomaly detection based on 3D-normality clusters
Savner et al. CrowdFormer: Weakly-supervised crowd counting with improved generalizability
Kavikuil et al. Leveraging deep learning for anomaly detection in video surveillance
Guo et al. Partially-sparse restricted boltzmann machine for background modeling and subtraction
Wei et al. Detecting video anomaly with a stacked convolutional LSTM framework
Hu et al. Video anomaly detection based on 3D convolutional auto-encoder
Roka et al. Anomaly behavior detection analysis in video surveillance: a critical review
Lalit et al. Crowd abnormality detection in video sequences using supervised convolutional neural network
Usha Rani et al. Real-time human detection for intelligent video surveillance: an empirical research and in-depth review of its applications
Wang et al. Intrusion detection for high-speed railways based on unsupervised anomaly detection models
Tripathy et al. A real-time two-input stream multi-column multi-stage convolution neural network (TIS-MCMS-CNN) for efficient crowd congestion-level analysis
Shafiee et al. Real-time embedded motion detection via neural response mixture modeling
Choo et al. Learning background subtraction by video synthesis and multi-scale recurrent networks
Almahadin et al. Enhancing video anomaly detection using spatio-temporal autoencoders and convolutional lstm networks
Wang et al. Mutuality-oriented reconstruction and prediction hybrid network for video anomaly detection
Pawar et al. Deep learning model based on cascaded autoencoders and one‐class learning for detection and localization of anomalies from surveillance videos
Behnaz et al. DEEPPBM: Deep probabilistic background model estimation from video sequences