Large animal detection and continuous traffic monitoring on highways

Mukherjee, Abir; Stolpner, Svetlana; Liu, X.; Vrenozaj, U.; Fei, C.; Sinha, Abhijit

doi:10.1109/icsens.2013.6688330

Cited by 14 publications

(15 citation statements)

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“…A review of research into collisions that involved an animal found that the indicators used to trigger the warning messages of conventional RADS systems [12–17] were limited to the presence/absence of an animal within a detection area. As safety indicators are usually designed to analyse vehicle‐to‐vehicle collisions [20] rather than animal‐vehicle collisions, additional indicators were required.…”

Section: Conceptual Framework For An Ng Radsmentioning

confidence: 99%

Developing a situation and threat assessment framework for a next generation roadside animal detection system

Mohammadi

Park

Mukherjee

et al. 2021

IET Intelligent Trans Sys

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Collisions involving large animals are a serious safety, economic and ecological concern. Some North American jurisdictions have installed a roadside animal detection system (RADS) that can warn the possible presence of large animals on rural highway sections. This study provides a conceptual framework for developing a next generation (NG) RADS. This study focuses on developing a process that can estimate the varying levels of threat posed by animals on the roadway using real‐time data on animal and vehicle positions. To estimate the level of threat, the study used a fuzzy rule‐based algorithm that integrates four input indicators (e.g., physical distance between animal and vehicle). The methodology was tested using real‐world traffic and animal data collected from a conventional RADS in British Columbia, Canada. The NG RADS has significant advantages over the conventional RADS. In particular, the NG RADS can disseminate varying levels of warning according to the estimated level of the threat rather than the constant level of warning generated by a conventional RADS. The NG RADS can also use a Vehicle‐to‐Infrastructure communication technology to establish direct wireless communication with vehicles at risk, for instance, to automatically control a vehicle's speed to avoid a collision with a large animal.

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Section: Conceptual Framework For An Ng Radsmentioning

confidence: 99%

Developing a situation and threat assessment framework for a next generation roadside animal detection system

Mohammadi

Park

Mukherjee

et al. 2021

IET Intelligent Trans Sys

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“…In [20] Large Animal Warning and Detection System has been analyzed. This system was designed to detect large animals such as moose and dear.…”

Section: Lawdsmentioning

confidence: 99%

Animal Detection: Techniques, Challenges and Future Scope

Kaur*,

Randhawa

2019

IJITEE

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 Abstract: Departments of transportation all across the world are concerned with ever increasing number of Animal-Vehicle Collisions as they cause thousands of human and animal causalities along with billions of economic losses every year. This is one of the few areas of transportation that safety is not improving. As more roads are being built, the areas that animal inhabit is shrinking and thus causing more crashes between vehicles and animals. The human fatalities and injuries, animal fatalities and injuries, and material costs of these crashes emphasize the need for a solution to this problem. Through this paper, new research directions and combination of technologies which are suitable for covering the research gaps are presented.

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“…A variety of systems apart from cameras have been used for detecting animals. A Large Animal Warning Detection System (LAWDS) making use of a 360 degree radar was proposed by Mukherjee et al [5].…”

Section: Literature Reviewmentioning

confidence: 99%

A vision based human - elepahant collision detection system

Dua

Shukla

Mittal

2015

2015 Third International Conference on Image Information Processing (ICIIP)

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Elephant intrusion in areas with high human movement can prove lethal for both human beings and elephants.The proposed system seeks to identify elephants with the aid of a Video Camera. The suggested methodology was applied to zones having high intervention of human beings and elephants. Regions with higher human movements like roads were extracted from the initial video frames. This process is followed by detecting motion in the video frame. The objects in the area of motion are then identified as elephant or non-elephant with the help of PHOG features and Support Vector Machines (SVM) classifiers.A dataset constituting of images of elephants and other objects was used for training the proposed algorithm. An overall accuracy of 85.29% was attained when static images containing elephants and other objects were classified. The same approach for detection was applied to identify moving elephants in the video frames. The proposed system endeavors to prevent casualties that occur in areas having high human elephant interaction.

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Large animal detection and continuous traffic monitoring on highways

Cited by 14 publications

References 0 publications

Developing a situation and threat assessment framework for a next generation roadside animal detection system

Developing a situation and threat assessment framework for a next generation roadside animal detection system

Animal Detection: Techniques, Challenges and Future Scope

A vision based human - elepahant collision detection system

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