Multi-user natural interaction with sensor on activity

Ladha, Shamsuddin; Smith‐Miles, Kate; Chandran, Sharat

doi:10.1109/uccv.2013.6530804

Cited by 5 publications

(11 citation statements)

References 12 publications

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“…Using active systems with laser devices permits to facilitate the extraction of accurate features based on the laser characteristics. In [8], authors proposed a new laser detection technique that combines several specifications such as brightness, size, aspect ratio, and gradual change of intensity from the center of laser spot. In [9], others use a combination of motion detection, shape recognition and histograms comparison methods in order to detect the laser spot.…”

Section: Related Workmentioning

confidence: 99%

Hardware Implementation and Validation of 3D Underwater Shape Reconstruction Algorithm Using a Stereo-Catadioptric System

et al. 2016

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Abstract:In this paper, we present a new stereo vision-based system and its efficient hardware implementation for real-time underwater environments exploration throughout 3D sparse reconstruction based on a number of feature points. The proposed underwater 3D shape reconstruction algorithm details are presented. The main concepts and advantages are discussed and comparison with existing systems is performed. In order to achieve real-time video constraints, a hardware implementation of the algorithm is performed using Xilinx System Generator. The pipelined stereo vision system has been implemented using Field Programmable Gate Arrays (FPGA) technology. Both timing constraints and mathematical operations precision have been evaluated in order to validate the proposed hardware implementation of our system. Experimental results show that the proposed system presents high accuracy and execution time performances.

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Section: Related Workmentioning

confidence: 99%

Hardware Implementation and Validation of 3D Underwater Shape Reconstruction Algorithm Using a Stereo-Catadioptric System

et al. 2016

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“…Our approach employs four known points for computing the homography matrix as employed in (Widodo et al, 2012;Ladha et al, 2013). Four points are taken from the center coordinates of four red markers as illustrated in Fig.…”

Section: Marker and Laser Detectionmentioning

confidence: 99%

Low Cost Shooting Simulator Based on a Single Board Computer

Soetedjo¹,

Mahmudi²,

Ashari³

et al. 2015

American Journal of Applied Sciences

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This paper proposes the camera on weapon system used as the shooting simulator. The single board computer (Raspberry Pi module) is used as the main processor. The OpenCV is implemented on the embedded system to perform the image processing task. The aim of the proposed camera on weapon system is to detect the position of the markers on the target and the laser spot. The detected coordinates are sent to the personal computer using a wireless communication provided by a low power Zigbee protocol. The performance of the proposed system using two camera systems (the Web-Camera and the Raspberry Pi-Camera) are evaluated. Both systems achieve the higher performances in the shooting detection which is greater than 85%, the target detection error which is lower than 0.8% and the small displacement on the arrangement of the camera and the laser pointer which is lower than one pixel. The Raspberry Pi-Camera system has a fast processing speed of 27 fps, while the processing speed of the Web-Camera system is 17 fps.

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“…In the second system, a camera is attached on the gun. Thus the camera moves along the shooter's movement [1,3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Three points on the target were used to solve the transformation matrix. In [3,6], four points were used to find the homography matrix. Since the position of camera is fixed, the homography transformation was calculated once at the callibration stage [2,6].…”

Section: Introductionmentioning

confidence: 99%

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Implementation of sensor on the gun system using embedded camera for shooting training

Soetedjo

Ashari

Mahmudi

et al. 2014

2014 2nd International Conference on Technology, Informatics, Management, Engineering &Amp; Environment

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This paper presents the implementation of an embedded camera system applied for shooting training, where the sensor on the gun system is adopted. A CMUcam4 is attached on the gun to detect the laser spot emitted by the shooter. In addition, the camera is used to detect the coordinates of marker on the target, which are sent to a computer for calculating the homography transform. A simple color thressholding is employed on the camera for such detection. Experiment results show that the errors of marker and laser spot detection are 2.33% and 2.15% respectively. The computer system helps to calculate the homography transform properly. Therefore the shooting point could be determined accurately, regardless of the position and viewing angle of the camera.

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Multi-user natural interaction with sensor on activity

Cited by 5 publications

References 12 publications

Hardware Implementation and Validation of 3D Underwater Shape Reconstruction Algorithm Using a Stereo-Catadioptric System

Hardware Implementation and Validation of 3D Underwater Shape Reconstruction Algorithm Using a Stereo-Catadioptric System

Low Cost Shooting Simulator Based on a Single Board Computer

Implementation of sensor on the gun system using embedded camera for shooting training

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