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A Novelty Detection Approach for Foreground Region Detection in Videos with Quasi-stationary Backgrounds

https://doi.org/10.1007/11919476_5

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11595755_41openalex-snapshot

Background Updating for Visual Surveillance

inferredcites →https://doi.org/10.1007/11595755_44openalex-snapshot

Automatic Robust Background Modeling Using Multivariate Non-parametric Kernel Density Estimation for Visual Surveillance

inferredcites →https://doi.org/10.1007/3-540-63930-6_174openalex-snapshot

Object tracking using adaptive colour mixture models

inferredcites →https://doi.org/10.1023/b:mach.0000008084.60811.49openalex-snapshot

Support Vector Data Description

inferredcites →https://doi.org/10.1109/34.598236openalex-snapshot

Pfinder: real-time tracking of the human body

inferredcites →https://doi.org/10.1109/34.868677openalex-snapshot

Learning patterns of activity using real-time tracking

inferredcites →https://doi.org/10.1109/34.868679openalex-snapshot

Detecting independent motion: the statistics of temporal continuity

inferredcites →https://doi.org/10.1109/cvpr.2003.1211454openalex-snapshot

Evaluation of local models of dynamic backgrounds

inferredcites →https://doi.org/10.1109/cvpr.2004.1315179openalex-snapshot

Motion-based background subtraction using adaptive kernel density estimation

inferredcites →https://doi.org/10.1109/iccv.1999.791228openalex-snapshot

Wallflower: principles and practice of background maintenance

inferredcites →https://doi.org/10.1109/icpr.1994.576243openalex-snapshot

Towards robust automatic traffic scene analysis in real-time

inferredcites →https://doi.org/10.1109/jproc.2002.801448openalex-snapshot

Background and foreground modeling using nonparametric kernel density estimation for visual surveillance

inferredcites →https://doi.org/10.1109/tpami.2005.102openalex-snapshot

Effective Gaussian mixture learning for video background subtraction

inferredcites →https://doi.org/10.48550/arxiv.1302.1539openalex-snapshot

Image Segmentation in Video Sequences: A Probabilistic Approach

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A Genetic Approach to Training Support Vector Data Descriptors for Background Modeling in Video Data

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EDAMOD: Edge Detection and Moving Object Detection Algorithm to Control Traffic Lights Dynamically

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Background subtraction via incremental maximum margin criterion: a discriminative subspace approach

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A survey on activity recognition and behavior understanding in video surveillance

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Background subtraction based on time-series clustering and statistical modeling

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Novelty detection in data streams

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A Computationally Economic Novel Approach for Real-Time Moving Multi-vehicle Detection and Tracking toward Efficient Traffic Surveillance

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Traditional and recent approaches in background modeling for foreground detection: An overview

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Background subtraction in real applications: Challenges, current models and future directions

inferred← citeshttps://doi.org/10.1016/j.neunet.2019.04.024openalex-snapshot

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

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LoPP: Locality Preserving Projections for Moving Object Detection

inferred← citeshttps://doi.org/10.1049/iet-cvi.2018.5376openalex-snapshot

Robust tracking of multiple objects in video by adaptive fusion of subband particle filters

inferred← citeshttps://doi.org/10.1109/cspa.2011.5759918openalex-snapshot

Moving vehicle segmentation in a dynamic background using self-adaptive kalman background method

inferred← citeshttps://doi.org/10.1109/cvprw.2009.5204109openalex-snapshot

Fuzzy statistical modeling of dynamic backgrounds for moving object detection in infrared videos

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SEVA: A Salient Event Detection Framework from Video Shots Using Support Vector Data Description

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Dynamic scene analysis using Kalman filter and mean shift tracking algorithms

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The application of cosine transform and principal components for foreground detection in video

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AI Camera for Tracking Road Accidents

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A Moving Object Detection Method Based on 3D Convolution Neural Network

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A Three-Way Optimization Technique for Noise Robust Moving Object Detection Using Tensor Low-Rank Approximation, l1/2, and TTV Regularizations

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A Background Modeling and Foreground Detection Algorithm Using Scaling Coefficients Defined With a Color Model Called Lightness-Red-Green-Blue

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Linear projective approach for moving object detection in video

inferred← citeshttps://doi.org/10.11606/t.55.2008.tde-02042008-112027openalex-snapshot

Detecção de novidade com aplicação a fluxos contínuos de dados

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Detecção de novidade em fluxos contínuos de dados multiclasse

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Traditional Approaches in Background Modeling for Static Cameras

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Novelty Detection in Data Streams

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Novelty Detection in Data Streams

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Key-Frame Detection and Video Retrieval Based on DC Coefficient-Based Cosine Orthogonality and Multivariate Statistical Tests

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Subspace Learning for Background Modeling: A Survey

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Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey

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Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

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Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

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Réseaux neuronaux en graphes inductifs Application à la surveillance Passive Visuelle et Acoustique

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