A survey of video processing techniques for traffic applications

Kastrinaki, V.; Zervakis, M.; Kalaitzakis, K.

doi:10.1016/s0262-8856(03)00004-0

Cited by 444 publications

(221 citation statements)

References 83 publications

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“…Although road vehicle tracking in traffic videos has considerable appeal for various applications, see Kastrinaki et al (2003) for this application domain in general or Sun et al (2006) for on-board vehicle detection and tracking, this manuscript focuses on basic research approaches to 3D-model-based tracking of vehicles recorded by a single stationary video camera. This specification excludes coverage of multi-ocular tracking, for example using a moving stereocamera pair as in Leibe et al (2006), as well as data-driven approaches, in particular ones operating only in the image plane.…”

Section: Discussion Of Related Publicationsmentioning

confidence: 99%

Initialization of Model-Based Vehicle Tracking in Video Sequences of Inner-City Intersections

Ottlik

Nagel

2007

Int J Comput Vis

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A fully automatic initialization approach for 3D-model-based vehicle tracking has been developed, based on Edge-Element and Optical-Flow association. An entire automatic initialization and tracking system incorporating this approach achieves results comparable to those obtained by earlier experiments based on semi-interactive initialization, provided the assessment criteria are roughly equivalent. Experiences with a large testing sample-about 15 minutes of inner-city traffic videos-are discussed in detail.

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Section: Discussion Of Related Publicationsmentioning

confidence: 99%

Initialization of Model-Based Vehicle Tracking in Video Sequences of Inner-City Intersections

Ottlik

Nagel

2007

Int J Comput Vis

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“…Blob analysis or blob tracking [1][2][3][4] is a method that is used to detect and track objects in which a blob is considered as an area of associated pixels. Blob Analysis typically involves the following steps: i) Background extraction, ii) Blob detection, iii) Blob analysis, iv) Blob tracking and v) Vehicle counting.…”

Section: Blob Analysis Based Tracking Techniquesmentioning

confidence: 99%

“…Hence, in most cases, intensity variation alone is not sufficient. Smoothness constraints facilitate the estimation of optical flow 3 . Thus, correlation based techniques determine the maximum shift around each pixel that maximizes the correlation of gray levels between consecutive frames.…”

Section: Optical Flow Based Tracking Techniquesmentioning

confidence: 99%

Vehicle detection for traffic flow analysis

Ramalingam

Varsani

2016

2016 IEEE International Carnahan Conference on Security Technology (ICCST)

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This paper looks at some of the algorithms that can be used for effective detection and tracking of vehicles, in particular for statistical analysis. The main methods for tracking discussed and implemented are blob analysis, optical flow and foreground detection. A further analysis is also done testing two of the techniques using a number of video sequences that include different levels of difficulties.

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“…Vision-based lane detection uses computer vision technologies to locate lane regions in road images which are captured from a moving camera. According to [1], vision-based lane detection techniques can be classified into three categories: region-based methods employ image segmentation algorithms to locate the area of road, featuredriven approaches tend to organize edges into a meaningful shape and model-based approaches match a template defining some characteristics of lanes to the observed image.…”

Section: Introductionmentioning

confidence: 99%

Parallel-Snake with Balloon Force for Lane Detection

Fang

2014

IEICE Trans. Inf. & Syst.

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SUMMARYLane detection plays an important role in Driver Assistance Systems and Autonomous Vehicle System. In this paper, we propose a parallel-snake model combined with balloon force for lane detection. Parallel-snake is defined as two open active contours with parallel constrain. The lane boundaries on the left and right sides are assumed as parallel curves, parallel-snake is deformed to estimate these two boundaries. As lane regions between left and right boundaries usually have low gradient, snake will lose external force on these regions. Furthermore, inspired by balloon active contour model, the balloon force is introduced into parallel-snake to expand two parallel curves from center of road to the left and right lane boundaries. Different from closed active contour, stretching force is adopted to prevent the head and tail of snake from converging together. The experimental results on three different datasets show that parallel-snake model can work well on images with shadows and handle the lane with broken boundaries as the parallel property.

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A survey of video processing techniques for traffic applications

Cited by 444 publications

References 83 publications

Initialization of Model-Based Vehicle Tracking in Video Sequences of Inner-City Intersections

Initialization of Model-Based Vehicle Tracking in Video Sequences of Inner-City Intersections

Vehicle detection for traffic flow analysis

Parallel-Snake with Balloon Force for Lane Detection

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