W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding overlapping components with MML
Statistics and Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Imaging Issue in an Automatic Face/Disguise Detection System
CVBVS '00 Proceedings of the IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (CVBVS 2000)
Face recognition with visible and thermal infrared imagery
Computer Vision and Image Understanding - Special issue on Face recognition
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Two-Stage Template Approach to Person Detection in Thermal Imagery
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Control of Adaptive Mixture-of-Gaussians Background Model
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Background-subtraction using contour-based fusion of thermal and visible imagery
Computer Vision and Image Understanding
Pedestrian detection and tracking in infrared imagery using shape and appearance
Computer Vision and Image Understanding
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
IR and visible light face recognition
Computer Vision and Image Understanding
Flexible background mixture models for foreground segmentation
Image and Vision Computing
Pedestrian tracking by fusion of thermal-visible surveillance videos
Machine Vision and Applications
A Dirichlet process mixture of generalized Dirichlet distributions for proportional data modeling
IEEE Transactions on Neural Networks
Asymmetric Generalized Gaussian Mixture Models and EM Algorithm for Image Segmentation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Unconstrained multiple-people tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Traffic video segmentation using adaptive-k gaussian mixture model
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Performance measures for video object segmentation and tracking
IEEE Transactions on Image Processing
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Splitting Gaussians in Mixture Models
AVSS '12 Proceedings of the 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance
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The interest in automatic surveillance and monitoring systems has been growing over the last years due to increasing demands for security and law enforcement applications. Although, automatic surveillance systems have reached a significant level of maturity with some practical success, it still remains a challenging problem due to large variation in illumination conditions. Recognition based only on the visual spectrum remains limited in uncontrolled operating environments such as outdoor situations and low illumination conditions. In the last years, as a result of the development of low-cost infrared cameras, night vision systems have gained more and more interest, making infrared (IR) imagery as a viable alternative to visible imaging in the search for a robust and practical identification system. Recently, some researchers have proposed the fusion of data recorded by an IR sensor and a visible camera in order to produce information otherwise not obtainable by viewing the sensor outputs separately. In this article, we propose the application of finite mixtures of multidimensional asymmetric generalized Gaussian distributions for different challenging tasks involving IR images. The advantage of the considered model is that it has the required flexibility to fit different shapes of observed non-Gaussian and asymmetric data. In particular, we present a highly efficient expectation-maximization (EM) algorithm, based on minimum message length (MML) formulation, for the unsupervised learning of the proposed model's parameters. In addition, we study its performance in two interesting applications namely pedestrian detection and multiple target tracking. Furthermore, we examine whether fusion of visual and thermal images can increase the overall performance of surveillance systems.