A real-time people counter

Conrad, Gary; Johnsonbaugh, Richard

doi:10.1145/326619.326649

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…[9] uses an array of PIR sensors in arranged in a line to detect the number of people through a flight of stairs. In [10], the center scanlines of an image frame is used for a similar effect. These approaches are used to detect people that pass through a confined area, and do not adapt well to open spaces.…”

Section: Background and Related Workmentioning

confidence: 99%

Lightweight People Counting and Localizing in Indoor Spaces Using Camera Sensor Nodes

Teixeira

Savvides

2007

2007 First ACM/IEEE International Conference on Distributed Smart Cameras

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This paper presents a lightweight method for localizing and counting people in indoor spaces using motion and size criteria. A histogram designed to filter moving objects within a specified size range, can operate directly on frame difference output to localize human-sized moving entities in the field of view of each camera node. Our method targets a custom, ultra-low power imager architecture operating on address-event representation, aiming to implement the proposed algorithm on silicon. In this paper we describe the details of our design and experimentally determine suitable parameters for the proposed histogram. The resulting histogram and counting algorithm are implemented and tested on a set of iMote2 camera sensor nodes deployed in our lab.

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Section: Background and Related Workmentioning

confidence: 99%

Lightweight People Counting and Localizing in Indoor Spaces Using Camera Sensor Nodes

Teixeira

Savvides

2007

2007 First ACM/IEEE International Conference on Distributed Smart Cameras

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“…Counting the number of people has been discussed widely in the literature. In 1994, Gary developed a real-time people counter using a fixed-camera [26]. The authors in [27] developed a deep convolution neural network (CNN) to count the number of people in extremely crowded areas for video surveillance.…”

Section: A Related Workmentioning

confidence: 99%

LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case

Eldeeb¹,

Alves²

2023

Preprint

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<p>In brief, we present a novel framework that counts the number of people based on the readings of the sensors inside closed rooms. These sensors are connected using LoRa network. First, we identify the transmission failures that cause missing values in the dataset and handle such missing values. In addition, we validate the efficiency of handling the missing values using a long short-term memory (LSTM) architecture that forecast future values based on the handled missing values. Finally, we propose a neural network that formulate the number of people based on the readings. Simulation results show our model achieves an accuracy of 95% in predicting the number of people. We believe that our work would have a significant impact on the road of IoT and wide area network research in wireless communications. Our work could benefit lockdown restrictions during pandemic eras and smart air ventilation systems in smart residual building.</p>

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“…They can reach a counting accuracy with more than 95% in a 200cm wide door. Conard et al discussed a real-time people counter in [4]. By taking images in a reasonably restricted traffic flow such as in the entry of a building, it can count people with 95% accuracy in a very crowded traffic environment.…”

Section: B People Countingmentioning

confidence: 99%

“…• V is the set of nodes representing rooms as well as hallways, stairways, and outside space; • E is a set of edges connecting rooms; In Figure 1, there are a total of six rooms r 1 , r 2 , r 3 , r 4 …”

Section: Modeling the Indoor Spacementioning

confidence: 99%

Tracking the Dynamic Distribution of People in Indoor Space with Noisy Partitioning Sensors

Wang

Huang

2012

2012 IEEE 13th International Conference on Mobile Data Management

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Abstract-The term "indoor" here refers generally to enclosed space partitioned into subspaces with connecting doors or gates. Examples include the inside of office buildings, amusement parks, and indoor shopping malls. In many applications, it is desirable to keep track of the distribution of people within the enclosed space. These applications range from smart house with automatically controlled air-conditioning and lighting, shopping assistance allocation, to business intelligence. Contact sensors are accurate but obstructive. Non-contact sensors such as automated visual recognition and RFID tags can be expensive in calibration or cost in order to obtain accurate readings. An interesting costoptimization problem is to understand how to obtain relatively accurate dynamic distribution of people from inaccurate point sensors using correlations among the point sensor readings. The paper formalizes a framework that uses a flow model and a particle-filter learning algorithm for this optimization problem based on the hypotheses that (1) there is an underlying stochastic "flow model" of people moving within the space, and (2) with the help of this flow model, counting of people can be made more accurate by taking advantage of the continuous, albeit inaccurate, point sensor readings. The main challenge is that the performance of particle filters deteriorates rapidly with the number of doors in the indoor space. We propose a divide and conquer method that uses relatively more accurate sensors at a few strategically chosen locations to achieve overall good accuracy. Experimental results given herein show that the algorithm is effective.

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A real-time people counter

Cited by 9 publications

References 7 publications

Lightweight People Counting and Localizing in Indoor Spaces Using Camera Sensor Nodes

Lightweight People Counting and Localizing in Indoor Spaces Using Camera Sensor Nodes

LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case

Tracking the Dynamic Distribution of People in Indoor Space with Noisy Partitioning Sensors

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