Contour tracking based on marginalized likelihood ratios

Pece, Arthur E. C.

doi:10.1016/j.imavis.2005.07.024

Cited by 9 publications

(8 citation statements)

References 58 publications

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“…The state estimate is updated by maximizing the postulated likelihood for observing grayvalue transitions. A recently published variant studies a marginalized likelihood model which takes into account possible deviations of vehicle shapes from the modeled one, see Pece (2006).…”

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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“…Therefore L(S str ) in (13) is the regularized log-likelihood of the working model (14). Similar objective functions in the form of likelihood ratios have been previous constructed by Pece [20] and references therein.…”

Section: The Objective Functionmentioning

confidence: 93%

Primal sketch: Integrating structure and texture

Guo

Zhu

2007

Computer Vision and Image Understanding

112

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“…The objective is to find this template back in each image considered, (Ballard, 1981;Ferryman et al, 1995;Tan et al, 1998;Pece and Worrall, 2002;Hinz, 2003;Zhao and Nevatia, 2003;Dahlkamp et al, 2004;Pece, 2006;Ottlik and Nagel, 2008). The disadvantage of model-based methods is the high dependency on geometric details of the considered object, which in our case would require that vehicles should appear in the image sequence with many details and with clear boundaries.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Detection From an Image Sequence Collected by a Hovering Helicopter

Nejadasl

Lindenbergh

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper addresses the problem of vehicle detection from an image sequence in difficult cases. Difficulties are notably caused by relatively small vehicles, vehicles that appear with low contrast or vehicles that drive at low speed. The image sequence considered here is recorded by a hovering helicopter and was stabilized prior to the vehicle detection step considered here. A practical algorithm is designed and implemented for this purpose of vehicle detection. Each pixel is identified firstly as either a background (road) or a foreground (vehicle) pixel by analyzing its gray-level temporal profile in a sequential way. Secondly, a vehicle is identified as a cluster of foreground pixels. The results of this new method are demonstrated on a test image-sequence featuring very congested traffic but also smoothly flowing traffic. It is shown that for both traffic situations the method is able to successfully detect low contrast, small size and low speed vehicles.

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Contour tracking based on marginalized likelihood ratios

Cited by 9 publications

References 58 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

Primal sketch: Integrating structure and texture

Vehicle Detection From an Image Sequence Collected by a Hovering Helicopter

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