An embedded system for counting passengers in public transportation vehicles

Bernini, Nicola; Bombini, Luca; Buzzoni, Michele; Cerri, Pietro; Grisleri, Paolo

doi:10.1109/mesa.2014.6935562

Cited by 30 publications

(13 citation statements)

References 12 publications

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“…In order to compare various algorithm variants, we executed each variant on the entire dataset. The results (cumulated for all sequences) are given in table 3: In and Out denote the number of passengers coming in/out the bus determined by the corresponding algorithm variant (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16), r In = In/GT In , and r Out = Out/GT Out are the ratios of In/Out and the ground-truth values, and δ denotes the error given as…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…Many approaches to estimating people flows utilize mechanical devices (gates, turnstiles, and other), and require passenger interaction and additional attention [2]. Although these devices are very robust and straightforward to install, they are of a high cost.…”

Section: Introductionmentioning

confidence: 99%

“…There exist a plethora of approaches for counting people in uncontrolled environments [9,2,8]. In a bunch of techniques, the problem is tackled by using multiple cameras to resolve the issues concerned with illumination changes, overcrowding, and occlusions in a scene by analyzing images acquired at different views [22,7,14].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time People Counting from Depth Images

Nalepa

Szymanek²,

Kawulok

2015

Beyond Databases, Architectures and Structures

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Abstract. In this paper, we propose a real-time algorithm for counting people from depth image sequences acquired using the Kinect sensor. Counting people in public vehicles became a vital research topic. Information on the passenger flow plays a pivotal role in transportation databases. It helps the transport operators to optimize their operational costs, providing that the data are acquired automatically and with sufficient accuracy. We show that our algorithm is accurate and fast as it allows 16 frames per second to be processed. Thus, it can be used either in real-time to process traffic information on the fly, or in the batch mode for analyzing very large databases of previously acquired image data.

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time People Counting from Depth Images

Nalepa

Szymanek²,

Kawulok

2015

Beyond Databases, Architectures and Structures

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show abstract

“…Automatic passenger counting is gradually becoming one of the more popular solutions with the emergence of surveillance camera/image based monitoring techniques [7] [8]. Such techniques rely on the camera's field of vision and hence are prone to be inaccurate with the presence of obstruction, poor light or overcrowded in the vehicle [9].Other sensing technologies have also been explored so far e.g. using doorway infrared sensor [10], RFID-sensor [11], smart ticket [12] etc.…”

Section: Introductionmentioning

confidence: 99%

Lessons learned using wi-fi and Bluetooth as means to monitor public service usage

Bai

Ireson

Mazumdar

et al. 2017

Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 AC

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Facets of urban public transport such as occupancy, waiting times, route preferences are essential to help deliver improved services as well as better information for passengers to plan their daily travel. The ability to automatically estimate passenger occupancy in near real-time throughout cities will be a step change in the way public service usage is currently estimated and provide significant insights to decision makers. The ever-increasing popularity and abundance of mobile devices with always-on Wi-Fi/Bluetooth interfaces makes Wi-Fi/Bluetooth sensing a promising approach for estimating passenger load. In this paper, we present a Wi-Fi/Bluetooth sensing system to detect mobile devices for estimating passenger counts using public transport. We present our findings on an initial set of experiments on a series of bus/tram journeys encapsulating different scenarios over five days in a UK metropolitan area. Our initial experiments show promising results and we present our plans for future large-scale experiments.

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“…It is divided into two categories: 1) people counting based on monocular camera [7]. 2) people counting based on stereo vision [8]. Methods based on monocular vision use two-dimensional data acquisition for target detection, tracking and counting.…”

Section: Introductionmentioning

confidence: 99%

Automatic Passenger Counting System for Bus Based on RGB-D Video

Feng¹,

Yang²,

Liang³

et al. 2017

Proceedings of the 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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Counting the number of passengers getting in/out of a bus in each station is a challenging task in smart bus field. To solve the problem, this paper presents a method of automatic people counting for a bus based on a RGB-D video, a zenithal camera in the bus door capturing the passengers flow is set, and the camera can get depth Image and RGB image simultaneously, we proposed a method combining RGB image and depth image to detect the head of the passengers, and then a novel tracking strategy is proposed. The test results show that this method can accurately extract the head area in the video and can accurately count the number of people getting in/out of a bus at a fast speed. The test data in different time periods show that the method has strong robustness and stability.

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An embedded system for counting passengers in public transportation vehicles

Cited by 30 publications

References 12 publications

Real-Time People Counting from Depth Images

Real-Time People Counting from Depth Images

Lessons learned using wi-fi and Bluetooth as means to monitor public service usage

Automatic Passenger Counting System for Bus Based on RGB-D Video

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