Comparative evaluation of stationary foreground object detection algorithms based on background subtraction techniques

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Á. Bayona, J. C. SanMiguel, and J. M. Martínez, "Comparative evaluation of stationary foreground object detection algorithms based on background subtraction techniques" in Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance. AVSS 2009, p. 25 - 30In several video surveillance applications, such as the detection of abandoned/stolen objects or parked vehicles,the detection of stationary foreground objects is a critical task. In the literature, many algorithms have been proposed that deal with the detection of stationary foreground objects, the majority of them based on background subtraction techniques. In this paper we discuss various stationary object detection approaches comparing them in typical surveillance scenarios (extracted from standard datasets). Firstly, the existing approaches based on background-subtraction are organized into categories. Then, a representative technique of each category is selected and described. Finally, a comparative evaluation using objective and subjective criteria is performed on video surveillance sequences selected from the PETS 2006 and i-LIDS for AVSS 2007 datasets, analyzing the advantages and drawbacks of each selected approach.This work has partially supported by the Cátedra UAMInfoglobal ("Nuevas tecnologías de vídeo aplicadas a sistemas de video-seguridad"), the Spanish Administration agency CDTI (CENIT-VISION 2007-1007), by the Spanish Government (TEC2007-65400 SemanticVideo), by the Comunidad de Madrid (S-050/TIC-0223- ProMultiDis), by the Consejería de Educación of the Comunidad de Madrid, and by The European Social Fund

A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain

Detecting camouflaged moving foreground objects has been known to be difficult due to the similarity between the foreground objects and the background. Conventional methods cannot distinguish the foreground from background due to the small differences between them and thus suffer from under-detection of the camouflaged foreground objects. In this paper, we present a fusion framework to address this problem in the wavelet domain. We first show that the small differences in the image domain can be highlighted in certain wavelet bands. Then the likelihood of each wavelet coefficient being foreground is estimated by formulating foreground and background models for each wavelet band. The proposed framework effectively aggregates the likelihoods from different wavelet bands based on the characteristics of the wavelet transform. Experimental results demonstrated that the proposed method significantly outperformed existing methods in detecting camouflaged foreground objects. Specifically, the average F-measure for the proposed algorithm was 0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI

Stationary foreground detection using background subtraction and temporal difference in video surveillance

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Á. Bayona, J. C. SanMiguel, and Martínez, "Stationary foreground detection using background subtraction and temporal difference in video surveillance", in 17th IEEE International Conference on Image Processing, ICIP 2010, p. 4657 - 4660In this paper we describe a new algorithm focused on obtaining stationary foreground regions, which is useful for applications like the detection of abandoned/stolen objects and parked vehicles. Firstly, a sub-sampling scheme based on background subtraction techniques is implemented to obtain stationary foreground regions. Secondly, some modifications are introduced on this base algorithm with the purpose of reducing the amount of stationary foreground detected. Finally, we evaluate the proposed algorithm and compare results with the base algorithm using video surveillance sequences from PETS 2006, PETS 2007 and I-LIDS for AVSS 2007 datasets. Experimental results show that the proposed algorithm increases the detection of stationary foreground regions as compared to the base algorithm.Work supported by the Spanish Government (TEC2007- 65400 SemanticVideo), by Cátedra Infoglobal-UAM for “Nuevas Tecnologías de video aplicadas a la seguridad”, by the Consejería de Educación of the Comunidad de Madrid and by the European Social Fund

Computer Vision Techniques for Background Modeling in Urban Traffic Monitoring

Jose Manuel Milla, Sergio Luis Toral, Manuel Vargas and Federico Barrero (2010). Computer Vision Techniques for Background Modeling in Urban Traffic Monitoring, Urban Transport and Hybrid Vehicles, Seref Soylu (Ed.), ISBN: 978-953-307-100-8, InTech, DOI: 10.5772/10179. Available from: http://www.intechopen.com/books/urban-transport-and-hybrid-vehicles/computer-vision-techniques-for-background-modeling-in-urban-traffic-monitoringIn this chapter, several background modelling techniques have been described, analyzed and tested. In particular, different algorithms based on sigma-delta filter have been considered due to their suitability for embedded systems, where computational limitations affect a real-time implementation. A qualitative and a quantitative comparison have been performed among the different algorithms. Obtained results show that the sigma-delta algorithm with confidence measurement exhibits the best performance in terms of adaptation to particular specificities of urban traffic scenes and in terms of computational requirements. A prototype based on an ARM processor has been implemented to test the different versions of the sigma-delta algorithm and to illustrate several applications related to vehicle traffic monitoring and implementation details

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset