Application Of Computer Vision Systems For Passenger Counting In Public Transport

Lengvenis, Paulius; Simutis, Rimvydas; Vaitkus, Vydandas; Maskeliūnas, Rytis

doi:10.5755/j01.eee.19.3.1232

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…Computer vision system memberikan akurasi perhitungan yang lebih baik dibandingkan infrared dan sensor tekanan hingga 95% [7]. Cara kerja dari computer vision adalah dengan meniru visual manusia dalam menafsirkan suatu obyek.…”

Section: Pendahuluanunclassified

Pemodelan Sistem Deteksi Wajah Sebagai Penghitung Jumlah Penumpang Transportasi Publik

Mukti¹,

Farokhah²,

Aqromi³

2021

jurnalresistor

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Bus is one of public transportation and as the most preferable by Indonesian to support their mobility. The high number of bus traffics then demands the bus management to provide the maximum service for their passenger, in order to gain public trust. Unfortunately, in the reality passenger list’s fraud is often faced by the bus management, there is a mismatch list between the amount of deposits made by bus driver and the number of passengers carried by the bus, and as the result it caused big loss for the Bus management. Automatic Passenger Counting (APC) then as an artificial intelligence program that is considered to cope with the bus management problems. This research carried out an APC technology based on passenger face detection using the Viola-Jones method, which is integrated with an embedded system based on the Internet of Things in the processing and data transmission. To detect passenger images, a webcam is provided that is connected to the Raspberry pi which is then sent to the server via the Internet to be displayed on the website provided. The system database will be updated within a certain period of time, or according to the stop of the bus (the system can be adjusted according to management needs). The system will calculate the number of passengers automatically; the bus management can export passenger data whenever as they want. There are 3 main points in the architecture of modeling system, they are information system design, device architecture design, and face detection mechanism design to calculate the number of passengers. A system design test is carried out to assess the suitability of the system being built with company needs. Then, based on the questionnaire distributed to the respondent, averagely 85.12 % claim that the Face detection system is suitability. The score attained from 4 main aspects including interactivity, aesthetics, layout and personalization

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Section: Pendahuluanunclassified

Pemodelan Sistem Deteksi Wajah Sebagai Penghitung Jumlah Penumpang Transportasi Publik

Mukti¹,

Farokhah²,

Aqromi³

2021

jurnalresistor

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“…The solutions based on the use of video cameras and applications on the basis of computer vision [18] and photogrammetry [19] are also under development. The process of development or purchase of such software is fairly resource intensive while accuracy of the calculation usually does not exceed 85-95 %.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Developing a controller for registering passenger flow of public transport for the "smart" city system

Boreiko

Teslyuk

2016

EEJET

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“…Lengvenis et al presented the passenger counter system created and installed in the Kaunas public city transport (Lengvenis et al, 2013); unlike other work based on edge features, this paper uses the vertical and horizontal projection of the scene using a top-view perspective, also known as bird's eye view. The results reported an accuracy of 86% for a single passenger getting on or off the bus, however it could not detect people passing each other or getting on a bus together and presented problems with lighting changes.…”

Section: Human Detectionmentioning

confidence: 99%

Coupling Camera-tracked Humans with a Simulated Virtual Crowd

Rivas

Gyves

Rudomín

et al. 2014

Proceedings of the 9th International Conference on Computer Graphics Theory and Applications

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Abstract:Our objective with this paper is to show how we can couple a group of real people and a simulated crowd of virtual humans. We attach group behaviors to the simulated humans to get a plausible reaction to real people. We use a two stage system: in the first stage, a group of people are segmented from a live video, then a human detector algorithm extracts the positions of the people in the video, which are finally used to feed the second stage, the simulation system. The positions obtained by this process allow the second module to render the real humans as avatars in the scene, while the behavior of additional virtual humans is determined by using a simulation based on a social forces model. Developing the method required three specific contributions: a GPU implementation of the codebook algorithm that includes an auxiliary codebook to improve the background subtraction against illumination changes; the use of semantic local binary patterns as a human descriptor; the parallelization of a social forces model, in which we solve a case of agents merging with each other. The experimental results show how a large virtual crowdreacts to over a dozen humans in a real environment.

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Application Of Computer Vision Systems For Passenger Counting In Public Transport

Cited by 20 publications

References 14 publications

Pemodelan Sistem Deteksi Wajah Sebagai Penghitung Jumlah Penumpang Transportasi Publik

Pemodelan Sistem Deteksi Wajah Sebagai Penghitung Jumlah Penumpang Transportasi Publik

Developing a controller for registering passenger flow of public transport for the "smart" city system

Coupling Camera-tracked Humans with a Simulated Virtual Crowd

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