Development of head detection and tracking systems for visual surveillance

Kang, Sung-Kwan; Chung, Kyungyong; Lee, Jung-Hyun

doi:10.1007/s00779-013-0668-9

Cited by 35 publications

(9 citation statements)

References 20 publications

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“…A recent trend among medical Q&A systems was to incorporate the organized medical information throughout Q&A process in order to utilize the information for efficient health management in various areas such as U-healthcare [40,41]. Jung et al [42] developed a decision supporting method mainly for pain management for chronic disease patients based on frequent pattern tree mining.…”

Section: Related Workmentioning

confidence: 99%

A Mobile‐Based Question‐Answering and Early Warning System for Assisting Diabetes Management

Xie

Ding

Yan³

et al. 2018

Wireless Communications and Mobile Computing

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With increasing demand for preventive management of chronic diseases in real time by using the Internet, interest in developing a convenient device on health management and monitoring has intensified. Unlike other chronic diseases, diabetes particularly type 2 is a lifelong chronic disease and usually requires daily health management by patients themselves. This study is to develop a mobilebased diabetes question-answering (Q&A) and early warning system named Dia-AID, assisting diabetes patients and populations at high risk. The Dia-AID system consists of three modules: a large-scale multilanguage diabetes frequently asked question repository, a multimode fusion Q&A framework, and a health data management module. A list of services including risk assessment and health early warning is provided to users for health condition monitoring. Using the diabetes frequently asked question repository as data, experiments are conducted on answer ranking and answer selection aspects. Results show that two essential methods in the system outperform baseline methods on both aspects.

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Section: Related Workmentioning

confidence: 99%

A Mobile‐Based Question‐Answering and Early Warning System for Assisting Diabetes Management

Xie

Ding

Yan³

et al. 2018

Wireless Communications and Mobile Computing

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“…We can also detect humans head, face and eye that are moving. We use motion segmentation to examine differences of moving persons face, and detect edge lines and restoration to find peoples face (Kang, et al, 2014). In addition to this, we adopt K-means to find head region and eye tracking to locate eyes.…”

Section: Related Workmentioning

confidence: 99%

Human concerned object detecting in video

Guo

Fan

2015

JAMDSM

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The purpose of our work is to detect the target human concerned in video. For security considerations, event detection in video has potential economic and social needs. Human concerned object detecting is very helpful for event detection. In some emergency or special events, people will focus on specific object. We need locate human body and face, detect the sight direction, and determine the object they are looking. Firstly, we divide video into several clips which have same scales, according to weight to segment or merge clips. After video segmentation, we can get some region. What is the concerned object(region) for the people in video? So we need to detect the human and face. We use HOG feature and SVM to classify human with different pose, and detect them out. On this basis, face detection and tracking is performed. We apply yaw and pitch angle to present sight direction, in line with the weight of sight to determine human concerned region. For the complex scenes, we do an articulation judgment and give up the irrelevant background, and highlight the prospects. Experiments show that our method not only applicable to simple scenes, but also complex background scenes, and shows clear and accurate result.

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“…For motion estimation, several authors have performed head gesture recognition based on eye tracking [6][7][8][9]. For instance, Choi & Rhee [6] and Kang et al [7] segmented the eyes by thresholding, and making use of the head's geometrical characteristics; Kapoor et al [8] used an infrared sensitive camera and infrared LEDs to track pupils; and Tan & Rong [9] used the cascade classifier from [10] to detect and track the eyes.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, Choi & Rhee [6] and Kang et al [7] segmented the eyes by thresholding, and making use of the head's geometrical characteristics; Kapoor et al [8] used an infrared sensitive camera and infrared LEDs to track pupils; and Tan & Rong [9] used the cascade classifier from [10] to detect and track the eyes. Nevertheless, a disadvantage of using the eyes as features for motion estimation, is the inability to detect and track faces with sunglasses (commonly worn by visually impaired people).…”

Section: Related Workmentioning

confidence: 99%

“…A common approach for this is to use HMMs to recognize each gesture. In fact, all the systems described before make use of two or three HMMs with different states [6][7][8][9][11][12][13], e.g., up/down for nodding and left/right for shaking. This approach works well if the system is in a fixed position (i.e., third-party perspective), and is commonly use in applications such as robotics and Human Computer Interaction (HCI), where the system should be able to infer the proper gestures and react accordingly.…”

Section: Related Workmentioning

confidence: 99%

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Robust Head Gestures Recognition for Assistive Technology

Terven

Salas

Raducanu

2014

Lecture Notes in Computer Science

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Abstract. This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.

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Development of head detection and tracking systems for visual surveillance

Cited by 35 publications

References 20 publications

A Mobile‐Based Question‐Answering and Early Warning System for Assisting Diabetes Management

A Mobile‐Based Question‐Answering and Early Warning System for Assisting Diabetes Management

Human concerned object detecting in video

Robust Head Gestures Recognition for Assistive Technology

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