Human Smoking Event Detection Using Visual Interaction Clues

Wu, Pin; Hsieh, Jun-Wei; Cheng, Jiun-Cheng; Cheng, Stone; Tseng, Shau-Yin

doi:10.1109/icpr.2010.1056

Cited by 39 publications

(26 citation statements)

References 12 publications

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“…al. [19] have shown the possibility of detecting smoking incidents from camera images. Most in line with the motivation of the work presented here is the report by Beard et.…”

Section: Related Workmentioning

confidence: 99%

When do you light a fire?

Scholl

Kücükyildiz

Laerhoven

2013

Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication

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An important step towards assessing smoking behavior is to detect and log smoking episodes in an unobtrusive way. Detailed information on an individual's consumption can then be used to highlight potential health risks and behavioral statistics to increase the smoker's awareness, and might be applied in smoking cessation programs. In this paper, we present an evaluation of two different monitoring prototypes which detect a user's smoking behavior, based on augmenting a lighter. Both prototypes capture and record instances when the user smokes, and are sufficiently robust and power efficient to allow deployments of several weeks. A real-world feasibility study with 11 frequently-smoking participants investigates the deployment and adoption of the system, hinting that smokers are generally unaware of their daily smoking patterns, and tend to overestimate their consumption.

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“…al. [19] have shown the possibility of detecting smoking incidents from camera images. Most in line with the motivation of the work presented here is the report by Beard et.…”

Section: Related Workmentioning

confidence: 99%

When do you light a fire?

Scholl

Kücükyildiz

Laerhoven

2013

Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication

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“…Recently, Pin Wu et al [1] proposed a method for detecting cigarettes to determine whether it is a smoking behavior or not. Because cigarettes must contact the lip when a smoking behavior happens, that paper searches the cigarette near the lip.…”

Section: Related Workmentioning

confidence: 99%

“…And H t-1 and H t denote the color histogram computed from F t-1 and F t respectively. Comparing H t-1 with H t , this system can get the ratio histogram R t , and the ratio histogram is defined as follows: (1) where, H t (.) is the color histogram of F t , and H t-1 (.)…”

Section: Handheld Detectionmentioning

confidence: 99%

The optical flow-based analysis of human behavior-specific system

Tsai

Chuang

Tseng

et al. 2013

2013 1st International Conference on Orange Technologies (ICOT)

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To detect illegal behaviors, cameras have to be mounted in public space to analyze human behavior to determine whether it is illegal. In this thesis, we focus on detecting smoking and drinking in certain public spaces as the illegal behaviors. Comparing with other works, our work does not need to establish the background in advance to classify human behavior.The proposed system consists of four modules including face region extraction, multiple hand samples extraction, features extraction, and behavior analysis. We extract three features from each human behavior. They are the touching time between the face and hand samples, smoke detection, and handheld object detection. Then a decision tree is employed to classify the human behavior by using the extracted three features. Experimental result demonstrates that the proposed method can be suited and successfully applied in many environments under various conditions, such as different illumination intensities, different backgrounds, and the different habits exhibited by human.

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“…Accelerometers have been used to measure the velocity of hand movements [7], and to recognize different arm gestures [8]. Infrared detectors have been used to identify directional movements of the arms [9], and capacitive sensors [10] and video [11, 12] have been used to locate the arm’s exact position. Additionally, the use of gyroscopes has also been studied to identify the characteristic angular velocities associated with a user taking a bite of a meal [13].…”

Section: Introductionmentioning

confidence: 99%

Detection of Hand-to-Mouth Gestures Using a RF Operated Proximity Sensor for Monitoring Cigarette Smoking

Lopez-Meyer

Patil²,

Tiffany

et al. 2013

TOBEJ

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Common methods for monitoring of cigarette smoking, such as portable puff-topography instruments or self-report questionnaires, tend to be biased due to conscious or unconscious underreporting. Additionally, these methods may change the natural smoking behavior of individuals. Our long term objective is the development of a wearable non-invasive monitoring system (Personal Automatic Cigarette Tracker – PACT) to reliably monitor cigarette smoking behavior under free living conditions. PACT monitors smoking by observing characteristic breathing patterns of smoke inhalations that follow a cigarette-to-mouth hand gesture. As envisioned, PACT does not rely on self-report or require any conscious effort from the user. A major element of the PACT is a proximity sensor that detects typical cigarette-to-mouth gesture during cigarette smoking. This study describes the design and validation of a prototype RF proximity sensor that captures hand-to-mouth gestures with a high sensitivity (0.90), and a methodology that can reject up to 68% of artifacts gestures originating from activities other than cigarette smoking.

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Human Smoking Event Detection Using Visual Interaction Clues

Cited by 39 publications

References 12 publications

When do you light a fire?

When do you light a fire?

The optical flow-based analysis of human behavior-specific system

Detection of Hand-to-Mouth Gestures Using a RF Operated Proximity Sensor for Monitoring Cigarette Smoking

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