Video Monitoring System: Counting People by Tracking

Nguyen, Dat T.; Huynh, Loc N.; Dinh, Thang Ba; Dinh, Tien Ba

doi:10.1109/rivf.2012.6169871

Cited by 5 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, this can be achieved through a combination of sensors, as shown in [54]. These sensors include passive-infrared (PIR) sensors [54] or more sophisticated camera based methods such as the methods shown on [55], where people can be actively counted to determine occupancy numbers and locations. PIR detectors are used in home alarm systems for motion detection ( i.e.…”

Section: Applicationsmentioning

confidence: 99%

The Role of Advanced Sensing in Smart Cities

Hancke

Silva

2012

Sensors

510

271

View full text Add to dashboard Cite

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.

show abstract

Section: Applicationsmentioning

confidence: 99%

The Role of Advanced Sensing in Smart Cities

Hancke

Silva

2012

Sensors

510

271

View full text Add to dashboard Cite

show abstract

“…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. Indoor examples for this task include using various videos/images such as from a monocular camera on top of a door [17], multiple cameras in smart environments [18,19], infrared and ultrasonic sensors [20,21], Wi-Fi signals [22][23][24][25], RFID [26], structural vibrational sensing [27], CO 2 sensors, and microphones [28]. All these methods might provide good results depending on the environment and fine-tuning, but sacrificing security/privacy of users (camera-based and Wi-Fi based solutions), having high computation overhead (camera-based solutions), or having low accuracy due to low data quality (ultrasonic-, infrared-, and RFID-based solutions, among others).…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Recently intelligent crowd counting has attracted researchers' attention in computer vision and related fields [1][2][3][4][5][6][7][8][9]. The existing predominant techniques for crowd counting fall into two categories: 1) object detection and tracking based crowd counting; 2) crowd density estimation based on features and regression analysis.…”

mentioning

confidence: 99%

An Improved Method of Crowd Counting Based on Regression

Jiang¹,

Zhao²

2013

Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)

View full text Add to dashboard Cite

Recently intelligent crowd counting has attracted researchers' attention in computer vision and related fields [1][2][3][4][5][6][7][8][9]. The existing predominant techniques for crowd counting fall into two categories: 1) object detection and tracking based crowd counting; 2) crowd density estimation based on features and regression analysis.In the first category work always involves pedestrian detection and tracking. Since the counting results depend heavily upon detection response, applying a state-of-the-art detector has becoming an essential key to improve system performance. In [1-4] generic detectors including HOG based headshoulder and JRoG based part detectors have been validated fairly effective in detecting and counting people. This kind of methods shows excellent performance in scenes with sparse crowd due to its accurate localization. However, as the crowd becomes large and heavy occlusion arises, individual detection and tracking both become almost impossible. Methods in the second category, such as [5][6][7], estimate the crowd density by extracting a set of holistic or local features in regions of interest (ROI) and then modeling the number of people based on features. This kind of methods does not consider individual localization and has been proved effective in dense crowd counting, especially in public environments where heavy occlusions happen frequently. In [8], the feature-regression based system is improved by applying human template matching. Antoni B. and Vasconcelos have shown in [9] that regression based crowd estimates are substantially more accurate than those produced by state-of-theart pedestrian detectors. Thus they are more generally used in crowd counting systems. and Yanyun ZhaoAbstr act An improved method of crowd counting based on regression is proposed to support intelligent management over crowd in video surveillance systems. According to the fact that human body has an articulate structure and complicated contours of shape, we propose a new low-level feature, the number of corner points, to highlight the describable capability of the feature set. We then introduce relevance vector regression (RVR) to model the correspondence between features and the pedestrian number, and propose a fusion scheme of RVR and Gaussian process regression (GPR) to further advance the performance of the proposed algorithm. Experimental results on two crowd datasets (one is UCSDpeds) demonstrate that the proposed work outperforms state-of-the-art methods and can fulfill the real-time requirement.

show abstract

Video Monitoring System: Counting People by Tracking

Cited by 5 publications

References 11 publications

The Role of Advanced Sensing in Smart Cities

The Role of Advanced Sensing in Smart Cities

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

An Improved Method of Crowd Counting Based on Regression

Contact Info

Product

Resources

About