Johnston's electrical and street railway directory

Mode of access: Internet.

Annual report of the Chicago, Indianapolis & Louisville Railway Company.

Published: Chicago: Stromberg, Allen & Co., 1900-

The Street railway journal.

Mode of access: Internet.

Map showing the Metropolitan Street Railway system and lines operated in connection therewith : 232.07 miles : [borough of Manhattan, New York City].

Covers Manhattan Island south of 166th Street.

Detailed exhibits of the physical property and intangible values of the South Chicago City Railway Company as of February 1, A.D. 1908; accompanying the valuation report submitted to the Committee on Local Transportation of the Chicago City Council,

"March, 1908."

Bulletin of the Indianapolis Public Library.

Description based on first issue; title from caption.

A dictionary of electric railway material.

Mode of access: Internet.

Electric railway journal.

Mode of access: Internet.

Second Welland Canal - Book 3, Survey Map 20 - Village of Port Colborne

Survey map of the Second Welland Canal created by the Welland Canal Company showing the areas in and around Port Colborne and Grantham Township. Identified structures associated with the Canal include Basin, Guard Lock, Two Lock Tender Houses, Lock House Lot, Collectors Office House, Towing Path, North and South Back Ditches, and land reserved for future improvemnt of basin. Surveyor measurements and notes can be seen in red and black ink as well as pencil. Local area landmarks dentified include Bridge, Rail Road Swing Bridge, Spoil Bank, Water Tank, Frazer Street Railway Station, Buffalo and Lake Huron Rail Road, Welland Rail Road, and land reserved for "Gardens for Lock Tenders". Local businesses identified include A.K Scholfield Store House Lot and Wharf, two stores and a tavern. Roads running parallel to Canal include King St., "present Travel Road", and the Southern Road Allowance. Roads running perpendicular to Canal include Kent St., Charlotte St., Clarence St., Princess St., Elgin St., George St., Frazer St., Alma St., Eastern Road Allowance. Properties and property owners are also identified and include P. White, John Flynn, George McMicking, Charles Carter, William H. Merritt, A.K. Scholfield, F. Gallgher, Ed McCabe, M. Smith, E. Lawder, J. Hanley, J. Harris, P. Gibbons, M. McGoveran, M. Madden, J. Hardison, T. Nihan, D. Gibbons, J. Cross, William Mellanby, Elis Gordon, Jane McCardy, L.G. Carter, T. Greenwood, C. Armstrong, J. McGillivray, T. Schofield, Mrs. Lanue, D. Mc_______, K. Minor, J. Manly and John McRae.

Automatic estimation of crowd density using texture

This paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of people's body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented. (C) 1998 Elsevier B.V. Ltd. All rights reserved.

Estimation of crowd density using image processing

Human beings perceive images through their properties, like colour, shape, size, and texture. Texture is a fertile source of information about the physical environment. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. This paper describes a new technique for automatic estimation of crowd density, which is a part of the problem of automatic crowd monitoring, using texture information based on grey-level transition probabilities on digitised images. Crowd density feature vectors are extracted from such images and used by a self organising neural network which is responsible for the crowd density estimation. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.

Estimating crowd density with Minkowski fractal dimension

The estimation of the number of people in an area under surveillance is very important for the problem of crowd monitoring. When an area reaches an occupation level greater than the projected one, people's safety can be in danger. This paper describes a new technique for crowd density estimation based on Minkowski fractal dimension. Fractal dimension has been widely used to characterize data texture in a large number of physical and biological sciences. The results of our experiments show that fractal dimension can also be used to characterize levels of people congestion in images of crowds. The proposed technique is compared with a statistical and a spectral technique, in a test study of nearly 300 images of a specific area of the Liverpool Street Railway Station, London, UK. Results obtained in this test study are presented.