A method for counting people attending large public events

Kopaczewski, Krzysztof; Szczodrak, Maciej; Czyżewski, Andrzej; Krawczyk, Henryk

doi:10.1007/s11042-013-1628-0

Cited by 22 publications

(10 citation statements)

References 14 publications

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“…This task can also be classified as indoor vs. outdoor. Outdoor examples include: (1) using photo-beam sensors in rural areas [13] where wireless sensor network connectivity can become a problem; (2) crowd-sourcing multiple cameras in big and crowded environments, such as events [14], where processing several video feed in real-time becomes a very costly computational operation; and (3) using a single camera in pedestrian walking areas [15,16], where both computational overhead and privacy issues in public areas emerge. These studies usually require high-performance devices or cameras (leading to privacy concerns) to make accurate calculations due to the requirements on the outdoor environments.…”

Section: Number Of People Estimationmentioning

confidence: 99%

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Singh

Akşanlı

2019

JSAN

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This paper presents a framework to accurately and non-intrusively detect the number of people in an environment and track their positions. Different from most of the previous studies, our system setup uses only ambient thermal sensors with low-resolution, using no multimedia resources or wearable sensors. This preserves user privacy in the environment, and requires no active participation by the users, causing no discomfort. We first develop multiple methods to estimate the number of people in the environment. Our experiments demonstrate that algorithm selection is very important, but with careful selection, we can obtain up to 100% accuracy when detecting user presence. In addition, we prove that sensor placement plays a crucial role in the system performance, where placing the sensor on the room ceiling yields to the best results. After accurately finding the number of people in the environment, we perform position tracking on the collected ambient data, which are thermal images of the space where there are multiple people. We consider position tracking as static activity detection, where the user's position does not change while performing activities, such as sitting, standing, etc. We perform efficient pre-processing on the data, including normalization and resizing, and then feed the data into well-known machine learning methods. We tested the efficiency of our framework (including the hardware and software setup) by detecting four static activities. Our results show that we can achieved up to 97.5% accuracy when detecting these static activities, with up to 100% class-wise precision and recall rates. Our framework can be very beneficial to several applications such as health-care, surveillance, and home automation, without causing any discomfort or privacy issues for the users.

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Section: Number Of People Estimationmentioning

confidence: 99%

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Singh

Akşanlı

2019

JSAN

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“…However, an extension of this system with a module for example for counting people entering and leaving a room, as described in [8,12,13], allows to obtain this information.…”

Section: A Chmielewska M Parzych T Marciniak a Dąbrowskimentioning

confidence: 99%

New Approach To Traffic Density Estimation Based On Indoor And Outdoor Scenes From CCTV

Chmielewska

Parzych

Marciniak

et al. 2015

Foundations of Computing and Decision Sciences

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Abstract. In this paper we present an algorithm for precise estimation of moving objects density (typically people and vehicles) in indoor and outdoor scenes. Automatic generation of the so-called density maps is based on video sequences acquired by surveillance systems. Our approach offers two types of solutions. The first one increments the accumulation table when a moving object is detected in a location of interest, delivering a density map of the presence of moving objects. The second algorithm increments the accumulation table only in cases of detecting a new moving object, resulting in a density map of the count of moving objects. The proposed algorithms were tested with the use of PETS 2009 database and with our own database of long-term video recordings. Finally, results of the density maps visualization and determination of the "busy hours" are presented.

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“…In [10], the authors presented a method to count people who are in a subway station using video surveillance cameras. Kopaczewski et al [11] introduced a method to estimate the number of people attending large public events , using the video signals gathered from multip le cameras and processing them with an efficient computer cluster. Knaian [12] developed a wireless sensor package to monitor roadways in the Intelligent Transportation Systems (ITSs) and count passing vehicles, as well as measure the average roadway speed, and detect ice and water on the road.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm to Count Nodes in Wireless Networks Using their Actual Position

Contreras¹,

Gamess²

2016

IJITCS

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Abstract-In this paper we introduce a novel algorith m for counting nodes based on wireless communicat ions and their actual position, which works for stationary nodes and in scenarios where nodes are moving at high speeds. For this, each node is endowed with a Global Positioning System (GPS) receptor, allo wing it to periodically send its actual position and speed through beacon messages. These data will be received by the firsthop neighboring nodes (which are within its scope or propagation range) that will have the ability to compute the actual position of the sending node based on the last broadcasted position and speed. The algorith m is constructed on the propagation of a count request message from the originator node toward nodes that are far away fro m it, and response messages traveling back to the originator, in the reverse path when it is possible, otherwise using the closest node on the way to the originator. To validate and evaluate the performance of our proposal, we simulate the algorith m using a famous network simulat ion tool called OMNeT++/INET. The results of our simulations show that the proposed algorith m efficiently co mputes a number of nodes very close to the real one, even in the case of scenarios of mobile nodes moving at high speeds, with an acceptable response time.

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A method for counting people attending large public events

Cited by 22 publications

References 14 publications

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

New Approach To Traffic Density Estimation Based On Indoor And Outdoor Scenes From CCTV

An Algorithm to Count Nodes in Wireless Networks Using their Actual Position

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