A novel video based system for detecting and counting vehicles at user-defined virtual loops

Barcellos, Pablo; Bouvie, Christiano; Escouto, Fabiano Lopes; Scharcanski, Jacob

doi:10.1016/j.eswa.2014.09.045

Cited by 50 publications

(29 citation statements)

References 14 publications

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“…However, there are several other active research areas that apply grayscale images. The simplicity of grayscale domain is commonly used in computer vision for moving object detection systems (Barcellos, Bouvie, Escouto, & Scharcanski, 2015;Karasulu & Korukoglu, 2012). Next, intelligent watermarking in biometric systems, that deals with grayscale texture masks, was discussed in Rabil, Tliba, Granger, and Sabourin (2013) whereas in Abdullah et al (2014) is presented a novel approach for face recognition by using Symmetric Local Graph Structure (SLGS).…”

Section: Introductionmentioning

confidence: 99%

Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization

Savic

Perić

Simic

2015

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization

Savic

Perić

Simic

2015

Expert Systems with Applications

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“…In complex traffic environments, the non-parametric approach that uses the Gaussian mixture density function ( Stauffer & Grimson, 1999 ), kernel-based mean-shift filter ( Comaniciu, Ramesh, & Meer, 2003 ), or the particle filter (PF) ( Aksel & Acton, 2010;Chan, Huang, Fu, Hsiao, & Lo, 2012;Isard & Blake, 1998a;1998b;Liu, Li, Wang, & Ni, 2015 ), has also been adopted to describe the nonlinear and non-Gaussian random processes of the moving objects. To improve the tracking performance, in addition to the pixel intensities of the moving objects, the color cues ( Barcellos, Bouvie, Escouto, & Scharcanski, 2015;Lehuger, Lechat, & Perez, 2006;Nummiaro, K-Meier, & Gool, 2002;Yin, Zhang, Sun, & Gu, 2011 ) or edge features ( Kumar & Sivanandam, 2012 ) have also been used in the traditional PF. Other statistical tracking algorithms include the nearest neighbor (NN), the multiple hypotheses tracking (MHT) ( Cox & Hingorani, 1996;Kim, Li, Ciptadi, & Rehg, 2015;Zulkifley & Moran, 2012 ) and the joint probabilistic data association filter (JPDAF) ( Shalom, Fortmann, & Cable, 1990 ).…”

Section: Introductionmentioning

confidence: 99%

Video-based tracking of vehicles using multiple time-spatial images

Mithun

Howlader

Rahman

2016

Expert Systems with Applications

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“…Recent traffic monitoring systems tend to use video cameras instead of more traditional traffic monitoring sensors (e.g., microwave or magnetic sensors), since video-based (or image-based) systems can provide important technological advantages. [2][3][4][5] Several camera-based systems have been proposed in the literature for traffic monitoring. Also, urban centers may already have installed cameras that can be used for monitoring the urban traffic, reducing installation costs.…”

Section: Introductionmentioning

confidence: 99%

Shadow detection in camera-based vehicle detection: survey and analysis

Barcellos

Gomes

Scharcanski

2016

J. Electron. Imaging

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Shadow detection in camera-based vehicle detection: survey and analysis,"Abstract. The number of vehicles in circulation in modern urban centers has greatly increased, which motivates the development of automatic traffic monitoring systems. Consequently, camera-based traffic monitoring systems are becoming more widely used, since they offer important technological advantages in comparison with traditional traffic monitoring systems (e.g., simpler maintenance and more flexibility for the design of practical configurations). The segmentation of the foreground (i.e., vehicles) is a fundamental step in the workflow of a camera-based traffic monitoring system. However, foreground segmentation can be negatively affected by vehicle shadows. This paper discusses the types of shadow detection methods available in the literature, their advantages, disadvantages, and in which situations these methods can improve camera-based vehicle detection for traffic monitoring. In order to compare the performance of these different types of shadow detection methods, experiments are conducted with typical methods of each category using publicly available datasets. This work shows that shadow detection definitely can improve the reliability of traffic monitoring systems, but the choice of the type of shadow method depends on the system specifications (e.g., tolerated error), the availability of computational resources, and prior information about the scene and its illumination in regular operation conditions. Barcellos, Gomes, and Scharcanski: Shadow detection in camera-based vehicle detection. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Barcellos, Gomes, and Scharcanski: Shadow detection in camera-based vehicle detection. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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A novel video based system for detecting and counting vehicles at user-defined virtual loops

Cited by 50 publications

References 14 publications

Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization

Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization

Video-based tracking of vehicles using multiple time-spatial images

Shadow detection in camera-based vehicle detection: survey and analysis

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