Hand Motion and Posture Recognition in a Network of Calibrated Cameras

Wang, Jingya; Payandeh, Shahram

doi:10.1155/2017/2162078

Cited by 6 publications

(7 citation statements)

References 46 publications

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“…For example, Tsai [ 20 ] and Zhang [ 21 ] proposed a planar patterned object in order to calibrate a single camera in a small field of view (FoV). Other calibration objects such as spheres were used for better analysis of camera calibration such as conic extraction that was proposed by Lu and Payandeh [ 22 , 23 ] and Wang and Payandeh [ 24 ].…”

Section: Background Materialsmentioning

confidence: 99%

“…For instance, Krumm et al [ 25 ] and Dai and Payandeh [ 26 ] demonstrated on how a single or multiple object can be labeled and tracked across multiple RGB surveillance cameras. Using two cameras, Wang and Payandeh [ 24 ] studied hand motion and posture recognition, and Gao et al [ 27 ] investigated a full body motion capture. In order to integrate data from multiple sensors, it is required that both their intrinsic parameters and their relative locations (i.e., extrinsic parameters) be determined in order to increase accuracy and correspondences between cameras [ 23 ].…”

Section: Background Materialsmentioning

confidence: 99%

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Passive Observer of Activities for Aging in Place Using a Network of RGB-D Sensors

Payandeh

Park

2020

International Journal of Telemedicine and Applications

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Aging in place is a notion which supports the independent living of older adults at their own place of residence for as long as possible. To support this alternative living which can be in contrast to various other types of assisted living options, modes of monitoring technology need to be explored and studied in order to determine a balance between the preservation of privacy and adequacy of sensed information for better estimation and visualization of movements and activities. In this paper, we explore such monitoring paradigm on how a network of RGB-D sensors can be utilized for this purpose. This type of sensor offers both visual and depth sensing modalities from the scene where the information can be fused and coded for better protection of privacy. For this purpose, we introduce the novel notion of passive observer. This observer is only triggered by detecting the absence of movements of older adults in the scene. This is accomplished by classifying and localizing objects in the monitoring scene from both before and after the detection of movements. A deep learning tool is utilized for visual classification of known objects in the physical scene followed by virtual reality reconstructing of the scene where the shape and location of objects are recreated. Such reconstruction can be used as a visual summary in order to identify objects which were handled by an older adult in-between observation. The simplified virtual scene can be used, for example, by caregivers or monitoring personnel in order to assist in detecting any anomalies. This virtual visualization can offer a high level of privacy protection without having any direct visual access to the monitoring scene. In addition, using the scene graph representation, an automatic decision-making tool is proposed where spatial relationships between the objects can be used to estimate the expected activities. The results of this paper are demonstrated through two case studies.

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Section: Background Materialsmentioning

confidence: 99%

Section: Background Materialsmentioning

confidence: 99%

Passive Observer of Activities for Aging in Place Using a Network of RGB-D Sensors

Payandeh

Park

2020

International Journal of Telemedicine and Applications

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“…e availability of low-cost small-sized cameras attached to wearable sensors and portable imaging devices has opened up a wide range of imaging-oriented applications, including assisted living, smart healthcare, traffic monitoring, virtual sports experiences, and posture recognition [1][2][3][4][5][6][7][8][9][10][11][12]. An interconnection of visual sensor nodes (sensor nodes with attached camera) is known as visual sensor network (VSN) [13,14] or as wireless multimedia sensor network (WMSN) [15,16].…”

Section: Motivationmentioning

confidence: 99%

Lifting-Based Fractional Wavelet Filter: Energy-Efficient DWT Architecture for Low-Cost Wearable Sensors

Tausif

Khan

Hasan

et al. 2020

Advances in Multimedia

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This paper proposes and evaluates the LFrWF, a novel lifting-based architecture to compute the discrete wavelet transform (DWT) of images using the fractional wavelet filter (FrWF). In order to reduce the memory requirement of the proposed architecture, only one image line is read into a buffer at a time. Aside from an LFrWF version with multipliers, i.e., the LFr WF m , we develop a multiplier-less LFrWF version, i.e., the LFr WF ml , which reduces the critical path delay (CPD) to the delay T a of an adder. The proposed LFr WF m and LFr WF ml architectures are compared in terms of the required adders, multipliers, memory, and critical path delay with state-of-the-art DWT architectures. Moreover, the proposed LFr WF m and LFr WF ml architectures, along with the state-of-the-art FrWF architectures (with multipliers (Fr WF m ) and without multipliers (Fr WF ml )) are compared through implementation on the same FPGA board. The LFr WF m requires 22% less look-up tables (LUT), 34% less flip-flops (FF), and 50% less compute cycles (CC) and consumes 65% less energy than the Fr WF m . Also, the proposed LFr WF ml architecture requires 50% less CC and consumes 43% less energy than the Fr WF ml . Thus, the proposed LFr WF m and LFr WF ml architectures appear suitable for computing the DWT of images on wearable sensors.

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“…Hand gesture recognition is significant for humancomputer interaction (HCI) and can simplify many essential applications in electronic devices, digital games, automobiles, and defense [1]. Hand gesture recognition has the potential to overcome dependency on the use of conventional HCI devices such as the mouse and keyboard [2].…”

Section: Introductionmentioning

confidence: 99%

“…In hand gesture recognition research, vision-based sensors such as depth cameras are widely used [2]. Optical sensors are one example of high-resolution sensors that help to track and recognize finger and wrist movements.…”

Section: Introductionmentioning

confidence: 99%

Hand Pointing Gestures Based Digital Menu Board Implementation Using IR-UWB Transceivers

2019

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Digital menu boards (DMB) are convenient for customers as well as sellers. In this paper, we have implemented a DMB using IR-UWB transceivers. Unlike the traditional touch-based interfaces for menu selection, in our proposed system, users can select items from the menu without touching the screen. The screen is used to display the menu, and the users point to the specific menu item to select it. Multiple radar transceivers are used to create a virtual space divided into different grid blocks in front of the digital display. Patterns in the radar data are analyzed using a multiclass support vector machine (SVM) classifier and a histogram of oriented gradient descriptor. The system is trained at two different distances from the radar sensors in order to make it robust against distance changes. The proposed hand pointing-based DMB system was verified through different experiments, with different grid sizes, to investigate accuracy dependence on grid size. The results showed high accuracy; therefore, the system can be used in real-life scenarios.INDEX TERMS Digital menu board (DMB), impulse radio ultrawideband, pattern analysis, gestures recognition.

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Hand Motion and Posture Recognition in a Network of Calibrated Cameras

Cited by 6 publications

References 46 publications

Passive Observer of Activities for Aging in Place Using a Network of RGB-D Sensors

Passive Observer of Activities for Aging in Place Using a Network of RGB-D Sensors

Lifting-Based Fractional Wavelet Filter: Energy-Efficient DWT Architecture for Low-Cost Wearable Sensors

Hand Pointing Gestures Based Digital Menu Board Implementation Using IR-UWB Transceivers

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