Pedestrian Tracking Based on Colour and Spatial Information

Seitner, Florian; Lovell, Brian C.

doi:10.1109/dicta.2005.64

Cited by 4 publications

(2 citation statements)

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“…First, the RGB input images are converted into HSV colour channels. The HSV colour space has been chosen because HSV is able to discriminate the colour and intensity information [31] and is easier to convert compared to the RGB colour space. After conducting several tests, only the value (V) component is included in the process of increasing the corneal edge because it produces an output that has the best contrast between the cornea and the sclera.…”

Section: Submentioning

confidence: 99%

Keratoconus Detection Using the Fusion Features of Anterior and Lateral Segment Photographed Images

et al. 2020

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Keratoconus (KC) is a type of eye disease that involves the thinning of the corneal layer and a change in the semispherical shape of the cornea to a bulging cone shape when viewed laterally. KC is difficult to detect in the early stages of the disease, as the patient does not feel any pain. Hence, the development of a KC detection (KD) method using a digital image processing approach is needed for the early detection of KC so that physicians can provide patients with the subsequent treatment sooner. The objective of this study was to develop a method of KD using a camera from a smart device to capture anterior and lateral segment photographed eye images (A&LSPIs). A total of 280 images comprising 140 KC and 140 normal A&LSPIs were used in this study, and all images were validated by a qualified expert. First, the corneal area of both image views was segmented so the geometric features could be extracted using the automated modified active contour model and the semiautomated spline function for the anterior and lateral images, respectively. Then, the features were selected using infinite latent feature selection (ILFS) by identifying the feature rankings based on the graph weighting that was automatically learned by the probabilistic latent semantic analysis (PLSA). The results showed that the all-combined features, where the proposed and improved features were successfully top ranked, had 96.05% accuracy, 98.41% sensitivity and 93.65% specificity with the RFn=50 classifier, outperforming the 7-NNMaha and SVMRBF classifiers. In conclusion, this study successfully developed a keratoconus detection system based on fusion features using a digital image processing approach for A&LSPIs captured with a smartphone camera.INDEX TERMS Keratoconus, lateral segment photographed images, anterior segment photographed images, fusion features, digital image processing

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Section: Submentioning

confidence: 99%

Keratoconus Detection Using the Fusion Features of Anterior and Lateral Segment Photographed Images

et al. 2020

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“…In addition, we initialise particle filtering by information obtained in the blob matching stage. Furthermore, a circular statistics method similar to the work by Seitner and Lovell [11] is proposed to analyze the hue component in HSV color space for the background model and the appearance model of humans. Finally, we extend a hierarchical chamfer matching system to detect pedestrians [3] through integration with particle filtering.…”

Section: Introductionmentioning

confidence: 99%

Classifying and tracking multiple persons for proactive surveillance of mass transport systems

Kong

Sanderson

Lovell

2007

2007 IEEE Conference on Advanced Video and Signal Based Surveillance

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We describe a pedestrian classification and tracking system that is able to track and label multiple people in an outdoor environment such as a railway station. The features selected for appearance modelling are circular colour histograms for the hue and conventional colour histograms for the saturation and value components. We combine blob matching with a particle filter for tracking and augment these algorithms with colour appearance models to track multiple people in the presence of occlusion. In the object classification stage, hierarchical chamfer matching combined with particle filtering is applied to classify commuters in the railway station into several classes. Classes of interest include normal commuters, commuters with backpacks, commuters with suitcases, and mothers with their children.

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