Bayesian Estimation of the Intensity for Independent Cluster Point Processes: An analytic solution

Swain, Anthony; Clark, Dan E.

doi:10.1016/j.proenv.2011.07.011

Cited by 4 publications

(1 citation statement)

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“…This paper applies a recently developed method for unified camera calibration and multi-target tracking and triangulation 1 for smart phone cameras based on a disparity space parameterisation 2-4 and a doubly-stochastic PHD filter. 5,6 The uniqueness of the procedure is the use of non-industrial grade cameras such as smart phones, portable web-cams etc as opposed to expensive industrial cameras. Moving object detection and tracking are the essential steps in object recognition, context analysis, indexing processes for visual surveillance systems, traffic monitoring and many other purposes.…”

Section: Introductionmentioning

confidence: 99%

Calibration of asynchronous smart phone cameras from moving objects

Hagen

Istenič

Bharti

et al. 2015

Twelfth International Conference on Quality Control by Artificial Vision 2015

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Calibrating multiple cameras is a fundamental prerequisite for many Computer Vision applications. Typically this involves using a pair of identical synchronized industrial or high-end consumer cameras. This paper considers an application on a pair of low-cost portable cameras with different parameters that are found in smart phones. This paper addresses the issues of acquisition, detection of moving objects, dynamic camera registration and tracking of arbitrary number of targets. The acquisition of data is performed using two standard smart phone cameras and later processed using detections of moving objects in the scene. The registration of cameras onto the same world reference frame is performed using a recently developed method for camera calibration using a disparity space parameterisation and the the single-cluster PHD filter.

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Section: Introductionmentioning

confidence: 99%