Handbook on "Background Modeling and Foreground Detection for Video Surveillance"

T. Bouwmans, F. Porikli, B. Hörferlin, A. Vacavant

  • Preface introducing the field of background modeling and foreground detection, and the different chapters:
This handbook solicited contributions to address these wide range of challenges met in background modeling and foreground detection for video-surveillance. Thus, it groups the works of the leading teams in this field over the recent years. By incorporating both existing and new ideas, this handbook gives a complete overview of the concepts, theories, algorithms, and applications related to background modeling and foreground detection. First, an introduction to background modeling and foreground detection for beginners is provided by surveying statistical models, clustering models, neural networks and fuzzy models. Furthermore, leading methods and algorithms for detecting moving objects in video surveillance are presented. A description of recent complete datasets and codes are given. Moreover, an accompanying website is provided. Finally, with this handbook, we aim to bring a one-stop solution, i.e., access to a number of different models, algorithms, implementations and benchmarking techniques in a single volume. The handbook consists of five parts.

This website contains the list of chapters, their abstract and links to the demos. It allows the reader to have a quick access to the main resources, datasets and codes in the field.

  • About the editors: T. Bouwmans, F. Porikli, B. Horferlin, A. Vacavant. (more details)
  • List of contributors:  The contributors are representative researchers in the field. (more details)
  • Dedication: The dedication is written by Chris Stauffer. (more details)
  • Foreword : The foreword is written by Thanarat Horprasert Chalidabhongse. (more details)
  • Contents (5 Parts, 25 Chapters)

Part I. Introduction and background

Chapter 1 - Traditional Approaches in Background Modeling for Static Cameras

T. Bouwmans (Lab MIA, Univ. La Rochelle, France)

Chapter 2 - Recent Approaches in Background Modeling for Static Cameras

T. Bouwmans (Lab MIA, Univ. La Rochelle, France)

Chapter 3 - Background Model Initialization for Static Cameras

L. Maddalena (National Research Council, Naples, Italy), A. Petrosino (Univ. of Naples Parthenope, Naples, Italy)

Chapter 4 - Background Subtraction for Moving Cameras

A. Elqursh (Rutgers University, USA) , A. Elgammal (Rutgers University, USA)

Part II. Traditional and Recent Models

Chapter 5 - Statistical Models for Background Subtraction

A. Elgammal (Rutgers University, USA)

Chapter 6 - Non-Parametric Background Segmentation with Feedback and Dynamic Controllers

P. Tiefenbacher (TUM, Munchen, Germany) , M. Hofmann (TUM, Munchen, Germany), G. Rigoll (TUM, Munchen, Germany)

Chapter 7 - ViBe: A Disruptive Method for Background Subtraction

M. Van Droogenbroeck (Belgium), O. Barnich (Belgium)

Chapter 8 - Online Learning by Stochastic Approximation for Background Modeling

E. Lopez (Univ. of Malaga, Spain), R. Luque (Univ. of Malaga, Spain)

Chapter 9 - Sparsity Driven Background Modeling and Foreground Detection

J. Huang (Univ. of Texas, USA), C. Chen (Univ. of Texas, USA), X. Cui (Facebook, USA)

Chapter 10 - Robust Detection of Moving Objects through Rought Set Theory Framework

P.Chiranjeevi (Indian Institute of Technology, India), S. Sengupta (Indian Institute of Technology, India)

Part III. Applications in Video Surveillance

Chapter 11 - Background Learning with Support Vectors: Efficient Foreground Detection and Tracking for Automated Visual Surveillance

A. Tavakkoli (Univ. of Houston-Victoria, USA), M. Nicolescu (CV Lab., Univ. of Nevada, USA), J. Wang (Microsoft Research, USA ), G. Bebis (CV Lab., Univ. of Nevada, USA)

Chapter 12 - Incremental Learning of an Infinite Beta-Louiville Mixture Model for Video Background Subtraction

W. Fan (CIISE, Concordia University, Canada), N. Bouguila (CIISE, Concordia University, Canada)

Chapter 13 - Spatio-temporal Background Models for Object Detection

S. Yoshinaga (LIMU, Kyushu University, Japan), Y. Nonaka (LIMU, Kyushu University, Japan), A. Shimada (LIMU, Kyushu University, Japan), H. Nagahara (LIMU, Kyushu University, Japan), R. Taniguchi  (LIMU, Kyushu University, Japan)

Chapter 14 - Background Modeling and Foreground Detection for Maritime Video Surveillance

D. Bloisi (Sapienza Univ. of Roma, Italy)

Chapter 15 - Hierarchical Scene Model for Spatial-color Mixture of Gaussians for Video Surveillance

C. Gabard (France), C. Achard (France), L. Lucas (France)

Chapter 16 - Online Robust Background Modeling via Alternating Grassmannian Optimization

J. He (Nanjing Univ. of Information Science and Technology, China), L. Balzano (Univ. of Michigan, USA), A. Szlam (City Univ. of New York, USA)

 Part IV. Sensors, Hardware and Implementations

Chapter 17 - Ubiquitous Imaging Sensors (light, thermal, range, radar)  for People Detection: An Overview

Z. Zivkovic (Netherlands)

Chapter 18 - 
RGB- D Cameras for Background-Foreground Segmentation

M. Camplani (Spain), L. Salgado (Spain)

Chapter 19  - Non Parametric GPU Accelerated Background Modeling of Complex Scenes

A. Morde (intuVision Inc., USA), S. Guler (intuVision Inc., USA)

Chapter 20  - GPU Implementation for Background/Foreground Separation via Robust PCA and Robust Subspace Tracking

C. Hage (TUM, Munchen, Germany) ,  F. Seidel (TUM, Munchen, Germany), M. Kleinsteuber (TUM, Munchen, Germany)

Chapter 21  -  Background Subtraction on Embedded Hardware

E. Fernandez-Sanchez (CITIC, Univ. of Granada, Spain), R.  Rodriguez-Gomez (CITIC, Univ. of Granada, Spain), J. Diaz (CITIC, Univ. of Granada, Spain), E. Ros
(CITIC, Univ. of Granada, Spain)

Chapter 22  -  Resource-efficient Foreground Detection with Embedded Smart Cameras

M. Casares (Syracuse University, USA), S. Velipasalar (Syracuse University, USA)

Part V. Benchmarking and Evaluation

Chapter 23 -  BGS Library: A Library Framework for Algorithm’s Evaluation in Foreground/Background Segmentation

A Sobral (Lab MIA, Univ. La Rochelle, France), T. Bouwmans (Lab MIA, Univ. La Rochelle, France)

Chapter 24 -  Overview and Benchmarking of Motion Detection Methods

P. Jodoin (Univ. of Sherbrooke, Canada), S. Piérard (Belgium), Y. Wang (Univ. of Sherbrooke, Canada),  M. Van Droogenbroeck (Belgium)

Chapter 25 -  Evaluation of Background Models with Synthetic and Real Data

A. Vacavant (France), T. Chateau (France), L. Tougne (France), L. Robinault  (France)