Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera

Kanhere, Neeraj Krantiveer; Pundlik, Shrinivas; Birchfield, Stan

doi:10.1109/cvpr.2005.365

Cited by 79 publications

(56 citation statements)

References 13 publications

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“…Nevertheless, real solutions shall consider as well the case of low angle views of the road, since it is not always possible to install the camera so high. Indeed, this issue has not been explicitly tackled by many researchers, being of particular relevance the work by [3], which is based on a feature tracking strategy.…”

Section: Related Workmentioning

confidence: 99%

“…In these works, the vehicle is simply treated as a set of foreground pixels [4], or as a set of feature points [2,3].…”

Section: Related Workmentioning

confidence: 99%

“…This is a relatively critical problem for single-camera setups. There are many works that do not pay special attention to this issue, which dramatically limits the impact of the proposed solutions in real situations [2][3][4].…”

Section: Related Workmentioning

confidence: 99%

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Vehicle tracking and classification in challenging scenarios via slice sampling

Nieto

Unzueta

Barandiarán

et al. 2011

EURASIP J. Adv. Signal Process.

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This article introduces a 3D vehicle tracking system in a traffic surveillance environment devised for shadow tolling applications. It has been specially designed to operate in real time with high correct detection and classification rates. The system is capable of providing accurate and robust results in challenging road scenarios, with rain, traffic jams, casted shadows in sunny days at sunrise and sunset times, etc. A Bayesian inference method has been designed to generate estimates of multiple variable objects entering and exiting the scene. This framework allows easily mixing different nature information, gathering in a single step observation models, calibration, motion priors and interaction models. The inference of results is carried out with a novel optimization procedure that generates estimates of the maxima of the posterior distribution combining concepts from Gibbs and slice sampling. Experimental tests have shown excellent results for traffic-flow video surveillance applications that can be used to classify vehicles according to their length, width, and height. Therefore, this vision-based system can be seen as a good substitute to existing inductive loop detectors.

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Section: Related Workmentioning

confidence: 99%

“…In these works, the vehicle is simply treated as a set of foreground pixels [4], or as a set of feature points [2,3].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle tracking and classification in challenging scenarios via slice sampling

Nieto

Unzueta

Barandiarán

et al. 2011

EURASIP J. Adv. Signal Process.

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“…A novel GMM algorithm applying in each libelling area to track the move of body parts in this area (Shi and Malik, 2000;Sasaki et al, 2009;Torralba et al, 2007). To avoid the error of ambiguous object's shape in some frame and object depth in the camera a texture template is used at each previse frame (Kanhere et al, 2005;Sarfraz et al, 2011;Aljuaid et al, 2010;). …”

Section: Jcsmentioning

confidence: 99%

Child Video Dataset Tool to Develop Object Tracking Simulates Babysitter Vision Robot

Aljuaid¹,

Mohamad²

2014

Journal of Computer Science

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This study presents a Child Video Dataset (CVDS) that has numerous videos of different ages and situation of children. To simulate a babysitter's vision, our application was developed to track objects in a scene with the main goal of creating a reliable and operative moving child-object detection system. The aim of this study is to explore novel algorithms to track a child-object in an indoor and outdoor background video. It focuses on tracking a whole child-object while simultaneously tracking the body parts of that object to produce a positive system. This effort suggests an approach for labeling three body sections, i.e., the head, upper and lower sections and then for detecting a specific area within the three sections and tracking this section using a Gaussian Mixture Model (GMM) algorithm according to the labeling technique. The system is applied in three situations: Child-object walking, crawling and seated moving. During system experimentation, walking object tracking provided the best performance, achieving 91.932% for body-part tracking and 96.235% for whole-object tracking. Crawling object tracking achieved 90.832% for body-part tracking and 96.231% for whole object tracking. Finally, seated-moving-object tracking achieved 89.7% for body-part tracking and 93.4% for whole-object tracking.

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“…More recently, [3] applies a feature tracking approach to traffic viewed from a low-angle off-axis camera. Vehicle occlusions and perspective effects pose a more significant challenge for a camera placed low to the ground.…”

Section: Related Workmentioning

confidence: 99%