Human Object Identification for Human-Robot Interaction by Using Fast R-CNN

Hsu, Shih-Chung; Wang, Yuwen; Huang, Chung-Lin

doi:10.1109/irc.2018.00043

Cited by 38 publications

(14 citation statements)

References 11 publications

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“…3. The value in this class is then transformed to the value of the soccer field coordinates with the following formula: = ( mod 9) × 75 (13) = (floor( / 9) × 75) + 37.5 (14) where mod is the modulo operation, floor is the round down operation, and and are robot coordinate points as the outputs of self-localization.…”

Section: Class To Soccer Field Coordinate Conversionmentioning

confidence: 99%

“…CNN framework for obstacle avoidance using a monocular webcam is also studied in [12]. For Human Object Identification for Human-Robot Interaction, Fast R-CNN is used in [13], and hybrid Fuzzy-CNN in [14]. Besides, the implementation of CNN has been used for many applications, such as for matching near duplicate image [15], for situation prediction and sentiment analysis [16,17], for handwritten recognition [18], and for human brain segmentation in medical [19].…”

Section: Introductionmentioning

confidence: 99%

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CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

Dikairono¹,

Setiawardhana²,

Purwanto³

et al. 2020

IJIES

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The Convolutional Neural Network (CNN) is an object classification method that has been widely used in recent research. In this paper, we propose CNN for use in the self-localization of wheeled soccer robots on a soccer field. If the soccer field is divided into equally sized quadrants with imaginary vertical and horizontal lines intersecting in the middle of the field, then the soccer field has an identical shape for each quadrant. Every quadrant is a reflection of the other quadrants. Superficially similar images appearing in different positions may result in positioning mistakes. This paper proposes a solution to this problem by using a visual modelling of the gyrocompass line mark and omni-vision image for the CNN-based self-localization system. A gyrocompass is used to obtain the angle of the robot on the soccer field. A 360° omni-vision camera is used to capture images that cover all parts of the soccer field wherever the robot is located. The angle of the robot is added to the omni-vision image using the visual modelling method. The implementation of self-localization without visual modelling gives accuracy rates of 0.3262, and this result is increased to 0.6827 with the proposed methods. The experiment was carried out in the robotics laboratory of the Institut Teknologi Sepuluh Nopember (ITS) with the ITS Robot with Intelligent System (IRIS) robot.

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Section: Class To Soccer Field Coordinate Conversionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

Dikairono¹,

Setiawardhana²,

Purwanto³

et al. 2020

IJIES

View full text Add to dashboard Cite

show abstract

“…The PID issue has been extensively studied in the computer vision and IoT fields. It plays a critical role in many security [13], surveillance [14], and business-intelligence [15] applications. The work [13] presents a smart homecare system by utilizing PID and behavior identification methods.…”

Section: Related Workmentioning

confidence: 99%

Things in the Air: Tagging Wearable IoT Informationon Drone Videos

Van

Zhang

Chang

et al. 2020

Preprint

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Drones have been applied to a wide range of security and surveillance applications recently. With drones, Internet of Things are extending to3D space. An interesting question is: Can we conduct person identification(PID) in a drone view? Traditional PID technologies such as RFID and fingerprint/iris/face recognition have their limitations or require close contactto specific devices. Hence, these traditional technologies can not be easily deployed to drones due to dynamic change of view angle and height. In this work,we demonstrate how to retrieve IoT data from users’ and correctly tag themon the human objects captured by a drone camera to identify and track groundhuman objects. First, we retrieve human objects from videos and conduct coordination transformation to handle the change of drone positions. Second,a fusion algorithm is applied to measure the correlation of video data andinertial data based on the extracted human motion features. Finally, we cancouple human objects with their wearable IoT devices, achieving our goal oftagging wearable device data such as personal profiles) on human objects ina drone view. Our experimental evaluation shows a recognition rate of 98.9%.To the best of our knowledge, this is the first work integrating videos fromdrone cameras and IoT data from inertial sensors.

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“…Since object detectors focus only on a portion of the image which contains the object they were trained on, they are suitable to recognize human figures and human body parts, like the hands or the face [8]. The literature shows that object detectors are intensely and competitively used in autonomous driving research, to detect not only other vehicles and street signs but also pedestrians [9].…”

Section: Vision Systems and Deep Learning: Related Workmentioning

confidence: 99%