A hardware architecture of face detection for human-robot interaction and its implementation

Lee, Sang-Seol; Jang, Sung-Joon; Kim, Jungho; Choi, Byeongho

doi:10.1109/icce-asia.2016.7804752

Cited by 6 publications

(8 citation statements)

References 3 publications

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“…The problem of camera pose estimation is egocentric, in other words, a single vector of 6 dimensions will suffice to relocalise the camera pose. 6DoF object detection [7] is also essential for robotic manipulation [31] and augmented reality applications [32]. The BOP benchmark [33] consists of eight datasets in a unified format that cover different practical scenarios and shows that the methods based on point-pair features currently outperform the methods based on template matching, learningbased and 3D local features.…”

Section: Related Workmentioning

confidence: 99%

End-to-End 6DoF Pose Estimation From Monocular RGB Images

Zou

Tian

et al. 2021

IEEE Trans. Consumer Electron.

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We present a conceptually simple framework for 6DoF object pose estimation, especially for autonomous driving scenarios. Our approach can efficiently detect the traffic participants from a monocular RGB image while simultaneously regressing their 3D translation and rotation vectors. The proposed method 6D-VNet, extends the Mask R-CNN by adding customised heads for predicting vehicle's finer class, rotation and translation. It is trained end-to-end compared to previous methods. Furthermore, we show that the inclusion of translational regression in the joint losses is crucial for the 6DoF pose estimation task, where object translation distance along longitudinal axis varies significantly, e.g. in autonomous driving scenarios. Additionally, we incorporate the mutual information between traffic participants via a modified non-local block to capture the spatial dependencies among the detected objects. As opposed to the original non-local block implementation, the proposed weighting modification takes the spatial neighbouring information into consideration whilst counteracting the effect of extreme gradient values. We evaluate our method on the challenging real-world Pascal3D+ dataset and our 6D-VNet reaches the 1st place in ApolloScape challenge 3D Car Instance task [1], [2].

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Section: Related Workmentioning

confidence: 99%

End-to-End 6DoF Pose Estimation From Monocular RGB Images

Zou

Tian

et al. 2021

IEEE Trans. Consumer Electron.

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“…This machine learning technique is the newest that has been implemented with FPGAs in our list (starting in 2005). It has been widely applied for image processing in face detection [524][525][526][527][528] and also for human detection [501,529].…”

Section: Machine Learningmentioning

confidence: 99%

Field Programmable Gate Array Applications—A Scientometric Review

2019

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Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers’ navigation systems. This paper reviews the top FPGAs’ applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications.

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“…Interaction and its Implementation [6] Researchers Sang-Seol Lee et al have developed a hardware based method for human-robot interaction using LBP. The Face Detection is done by first resizing the image followed by generation of LBP for detecting performance and finding particular case of texture spectrum in the image and then using AdaBoost-based 4 cascade classifications to decide FD regions with an associated confidence value.…”

Section: A Hardware Architecture Of Face Detection For Human-robotmentioning

confidence: 99%

Face Detection and Recognition Using Haar Classifier and LBP Histogram

Das¹,

Gangopadhyay²,

Chatterjee³

2018

ijarcs

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Abstract:Facial recognition technology is the process for identifying or verifying a face from digital images. The need for face recognition has been of real importance with the development of modern society. Detection and recognition of faces has been on the rise worldwide owing the requirement for security for economic transactions, authorization, national safety and security and other important factors. The technology comprises of face detection, database creation and face recognition. This paper presents a new approach of face identification using LBP method and Haar-like features. The first step is face detection which is done using Haar cascade classifier. After detection, a face is saved in the database. Then the faces from the database are passed through the face recognition algorithm. The Local Binary Pattern Histogram (LBPH) method is used for face recognition. The performance of face detection can be seen to produce maximum error of 1.6%, 2.1% and 0.8% in case of Real-Time video, image file and video file respectively which may be considered accurate. The recognition algorithm produces maximum error of 0.4% which may be considered accurate as well.

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A hardware architecture of face detection for human-robot interaction and its implementation

Cited by 6 publications

References 3 publications

End-to-End 6DoF Pose Estimation From Monocular RGB Images

End-to-End 6DoF Pose Estimation From Monocular RGB Images

Field Programmable Gate Array Applications—A Scientometric Review

Face Detection and Recognition Using Haar Classifier and LBP Histogram

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