Counting People by Using Convolutional Neural Network and A PIR Array

Tsou, Peng-Rong; Wu, Cheng-En; Chen, Yen-Ru; Ho, Y. E. Janice; Chang, Junkai; Tsai, Hsiao-Ping

doi:10.1109/mdm48529.2020.00075

Cited by 13 publications

(9 citation statements)

References 9 publications

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“…However, smartphones broadcast BLE beacons only when requested by the user for security reasons, which makes smartphones unviable for crowd counting. A passive infrared (PIR) sensor array might be another option since it has been used for counting occupants [ 17 , 18 ]. However, PIR-based techniques work only in delicately designed indoor environments since the raw PIR signal is notoriously noisy [ 19 ].…”

Section: Microwdmentioning

confidence: 99%

MiCrowd: Vision-Based Deep Crowd Counting on MCU

Son

Seo

Gyeongseon

et al. 2023

Sensors

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Microcontrollers (MCUs) have been deployed on numerous IoT devices due to their compact sizes and low costs. MCUs are capable of capturing sensor data and processing them. However, due to their low computational power, applications processing sensor data with deep neural networks (DNNs) have been limited. In this paper, we propose MiCrowd, a floating population measurement system with a tiny DNNs running on MCUs since the data have essential value in urban planning and business. Moreover, MiCrowd addresses the following important challenges: (1) privacy issues, (2) communication costs, and (3) extreme resource constraints on MCUs. To tackle those challenges, we designed a lightweight crowd-counting deep neural network, named MiCrowdNet, which enables on-MCU inferences. In addition, our dataset is carefully chosen and completely re-labeled to train MiCrowdNet for counting people from an mobility view. Experiments show the effectiveness of MiCrowdNet and our relabeled dataset for accurate on-device crowd counting.

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Section: Microwdmentioning

confidence: 99%

MiCrowd: Vision-Based Deep Crowd Counting on MCU

Son

Seo

Gyeongseon

et al. 2023

Sensors

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“…Second, a Hidden Markov Model (HMM) refines the estimate, while taking into consideration the uncertainty of the sensors. Furthermore, Tsou et al (2020) designed a people counting device that combines an array of 16 PIR sensors. Positioned on the ceiling over an entrance, this device collects passingbys data which is first used for collecting training data for a Convolutional Neural Network (CNN).…”

Section: Related Workmentioning

confidence: 99%

On the people counting problem in smart homes: undirected graphs and theoretical lower-bounds

Giaretta

Loutfi

2021

J Ambient Intell Human Comput

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Smart homes of the future will have to deal with multi-occupancy scenarios. Multi-occupancy systems entail a preliminary and critical feature: the capability of counting people. This can be fulfilled by means of simple binary sensors, cheaper and more privacy preserving than other sensors, such as cameras. However, it is currently unclear how many people can be counted in a smart home, given the set of available sensors. In this paper, we propose a graph-based technique that allows to map a smart home to an undirected graph G and discover the lower-bound of certainly countable people, also defined as certain count. We prove that every independent set of n vertices of an undirected graph G represents a minimum count of n people. We also prove that the maximum number of certainly countable people corresponds to the maximum independent sets of G, and that the maximal independent sets of G provide every combination of active sensors that ensure different minimum count. Last, we show how to use this technique to identify and optimise suboptimal deployment of sensors, so that the assumptions can be tightened and the theoretical lower-bound improved.

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“…In systems related to security, occupancy estimation is often used to detect intrusions [4,5], while occupancy detection in industrial workplaces can be used to prevent injuries by raising an alarm when people are detected in unsafe areas [6]. In the event of a fire, especially in high-rise buildings, firefighters can benefit from real-time room occupancy estimates when planning evacuation and rescue missions [7]. Facility managers can use data of historical room usage to gain insight as to whether rooms and buildings are being utilized as intended or not, thus aiding them in their choice of which room types to prioritize in changes or new developments.…”

Section: Introductionmentioning

confidence: 99%

“…Depatla and Mostofi showed that it is possible to estimate the number of people in an indoor space by transmitting WiFi signals into a room and measure what signal is received at the opposite side of the room after the signal has propagated through the room [11]. Tsou et al, Leech et al, and Raykov et al used passive infrared, PIR, sensor data together with supervised learning to estimate occupancy [7,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Many of the articles concerned with automated people count estimation rely on some form of supervised machine learning algorithm [7,8,12,13]. In other words, using a labeled dataset with known people counts for every sensor observation, the authors develop a model that learns from the dataset what the correct mapping is between sensor data (PIR data, images from a camera, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Synthetic Generation of Passive Infrared Motion Sensor Data Using a Game Engine

Stefansson

Karlsson

Persson

2021

Sensors

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Quantifying the number of occupants in an indoor space is useful for a wide variety of applications. Attempts have been made at solving the task using passive infrared (PIR) motion sensor data together with supervised learning methods. Collecting a large labeled dataset containing both PIR motion sensor data and ground truth people count is however time-consuming, often requiring one hour of observation for each hour of data gathered. In this paper, a method is proposed for generating such data synthetically. A simulator is developed in the Unity game engine capable of producing synthetic PIR motion sensor data by detecting simulated occupants. The accuracy of the simulator is tested by replicating a real-world meeting room inside the simulator and conducting an experiment where a set of choreographed movements are performed in the simulated environment as well as the real room. In 34 out of 50 tested situations, the output from the simulated PIR sensors is comparable to the output from the real-world PIR sensors. The developed simulator is also used to study how a PIR sensor’s output changes depending on where in a room a motion is carried out. Through this, the relationship between sensor output and spatial position of a motion is discovered to be highly non-linear, which highlights some of the difficulties associated with mapping PIR data to occupancy count.

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Counting People by Using Convolutional Neural Network and A PIR Array

Cited by 13 publications

References 9 publications

MiCrowd: Vision-Based Deep Crowd Counting on MCU

MiCrowd: Vision-Based Deep Crowd Counting on MCU

On the people counting problem in smart homes: undirected graphs and theoretical lower-bounds

Synthetic Generation of Passive Infrared Motion Sensor Data Using a Game Engine

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