Real-Time System for Counting the Number of Passing People Using a Single Camera

Kim, Jae Won; Choi, Kang-Sun; Choi, Byung-Il; Lee, Jae Yong; Ko, Sung Jea

doi:10.1007/978-3-540-45243-0_60

Cited by 24 publications

(11 citation statements)

References 7 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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“…The LD PeCo People Counter manufactured by SICK uses double vertical laser curtain and counts people based on their height. Video cameras based system uses image processing techniques to estimate the number of people [10]. The image processing steps can be decomposed into detecting phase, tracking phase and interpreting phase [4].…”

Section: Related Workmentioning

confidence: 99%

An Echo State Network based pedestrian counting system using wireless sensor networks

Mathews

Poigné

2008

2008 International Workshop on Intelligent Solutions in Embedded Systems

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A pedestrian counter has a lot of applications like effective resource utilization, planning of service activities and ensuring safety and convenience. The design and implementation of a new intelligent Pedestrian Counter is presented in this paper. The counter is publicly usable, low cost, easily deployable and scalable. We used off-the-shelf components for our design and the overall cost is less than 200 ¿. The counter works in distributed mode and has wireless communication facilities. The hardware platform consists of PIR sensor units that detect the pedestrian movements, the wireless sensor nodes that handle the sensor data acquisition and transmission and the base station computer that process the data. We have trained an Echo State Network and this Recurrent Neural Network functions as the brain for the the Pedestrian Counter. A system model was constructed using Simulink for generating the training data for the neural network. A modular layered software framework was designed for processing the pedestrian counter data. The Echo State Network successfully learned the various motion patterns and the pedestrian counter gave a reasonably good performance of 80.4%. To improve the performance further, redesigning the systems using low cost Active IR distance sensor is suggested.

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“…The search range of FD can be reduced by considering the area related to the moving objects. To extract moving objects, background subtraction [4] can be applied. Using the stereo image recognition system [5], the distance between each object and the cameras is obtained.…”

Section: A Preprocessingmentioning

confidence: 99%

Real-Time Person Identification System for Intelligent Digital TV

Hwang

Kim

et al. 2007

2007 Digest of Technical Papers International Conference on Consumer Electronics

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Intelligent digital TV (iDTV) is an enhanced digital TV that can automatically provide user-personalized services for each audience. For the user-personalized services, the iDTV should recognize audiences in real-time. Thus, in this paper, we define a novel structure of the iDTV and propose a real-time person identification system embedded in the iDTV. The proposed system consists of three phases: preprocessing for reducing computational costs of the proposed system, face detection using a statistical approach with Haar-like features, and face recognition using Support Vector Machines (SVMs). Experimental results show that the proposed system achieves efficient performance with high recognition accuracy of above 90% at the speed of 15~20 fps, which is suitable for applying to the iDTV.

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Real-Time System for Counting the Number of Passing People Using a Single Camera

Cited by 24 publications

References 7 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

An Echo State Network based pedestrian counting system using wireless sensor networks

Real-Time Person Identification System for Intelligent Digital TV

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