Segmentation and tracking of piglets in images

McFarlane, N.J.B.; Schofield, C. P.

doi:10.1007/bf01215814

Cited by 466 publications

(224 citation statements)

References 4 publications

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“…Different background subtraction methods were considered in this work. These include Frame Differencing, Approximate Median [8] and Running Average. However, the background subtraction technique which we propose involves the subtraction of an initial static background from the current frame.…”

Section: Motion Detectionmentioning

confidence: 99%

Vision based surveillance system

Attard

Farrugia

2011

2011 IEEE EUROCON - International Conference on Computer as a Tool

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Abstract-Due to the numerous amounts of surveillance cameras available, security guards seem to be ubiquitously watching over. However, the number of existing cameras exceeds the number of humans to monitor them and the supervision of all the sensors' output is costly. Thus, video footage from cameras is most often only used as a forensic tool. This suggests the need of an intelligent video surveillance system providing continuous 24-hour monitoring, replacing the traditional ineffective systems.This paper presents an automated vision based surveillance system which is capable to detect and track humans and vehicles from a video footage. Simulation results have shown that the Object Classification module manages to achieve an accuracy of 97.31% and 97.14% for the person and vehicle classification respectively. Furthermore, the system manages to successfully track the objects 97% of the time under no occlusion and 94.14% in presence of occlusion.

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Section: Motion Detectionmentioning

confidence: 99%

Vision based surveillance system

Attard

Farrugia

2011

2011 IEEE EUROCON - International Conference on Computer as a Tool

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“…A temporal median filter can be used to estimate a color-based background model [110]. One can also generalize to other features such as color histograms [84,196] and local self-similarity features [70].…”

Section: Basic Modelsmentioning

confidence: 99%

Background Subtraction on Embedded Hardware

Jodoin¹,

Pierard

Wang³

et al. 2014

Background Modeling and Foreground Detection for Video Surveillance

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“…Based on approximated median filtering (MF), McFarlane [13] proposed a background model which stores only one video frame that is the background image for object detection. The strength of the algorithm is that it has a very low computational time as compared to most of the background updating algorithms and again does not require a buffer to store the last n frames.…”

Section: Related Workmentioning

confidence: 99%

“…If a scene remains stationary for a long period of time, the variances of the background components may become very small. A sudden change in global illumination can then turn the entire frame into foreground [11].Stauffer et al [13,14] proposed Gaussian Mixture Model (GMM) to model the value of each pixel of the video frames as a mixture of Gaussians. This method can solve the problems of light changes and waving trees in the background, but the parameters of the Gaussian distributions need to be estimated and optimized in advance, which will lead to high computational complexity.…”

Section: Related Workmentioning

confidence: 99%

A Robust Median-based Background Updating Algorithm

ObedAppiah¹,

Hayfron-Acquah²

2017

IJIGSP

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Abstract-Image processing techniques for object tracking, identification and classification have become common today as a result of improved quality of cameras as well as prices of cameras becoming cheaper and cheaper day by day. The use of cameras also make it possible for human analysis of video streams or images where it is difficult for robots or algorithms or machines to effectively deal with the images. However, the use of cameras for basic tracking and analysing do not come without challenges such as issues with sudden changes in illumination, shadows, occlusion, noise, and high computational time and space complexities of algorithms. A typical image processing task may involve several subtasks such as capturing, and pre-processing which demand high computational resources to complete. One of the main pre-processing tasks used in image processing is image segmentation which enables images to be divided into sections of interest in order to perform analysis on them. Background Subtraction is commonly used to segment images into Background and Foreground for further processing. Algorithms producing highly accurate results during this segmentation task normally demand high computation time or memory space, while algorithms that use smaller memory space and shorter time to complete this segmentation task may also suffer from limitations that may lead to undesired results at some point in time. Poor outputs from algorithms will eventually lead to system failure which must be avoided as much as possible. This paper proposes a median based background updating algorithm which determines the median of a buffer containing values that are highly correlated. The algorithm achieves this by deletingan extreme valuefrom the buffer whenever data is to be added to it.Experiments show that the method produces good results with less computational time which will make it possible to implement on devices that do not have much computation resources.

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Segmentation and tracking of piglets in images

Cited by 466 publications

References 4 publications

Vision based surveillance system

Vision based surveillance system

Background Subtraction on Embedded Hardware

A Robust Median-based Background Updating Algorithm

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