HOG-AdaBoost Implementation for Human Detection Employing FPGA ALTERA DE2-115

Adiono, Trio; Prakoso, Kevin Shidqi; Putratama, Christoporus Deo; Yuwono, Bramantio

doi:10.14569/ijacsa.2018.091042

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…In the HOG algorithm, the derivative values (dx and dy) are computed for every pixel using convolution kernel as (1). Since we utilize cell-based calculation, there will be many edges within a window [29].…”

Section: Cell Derivates With Edge Neighboring Anti-aliasingmentioning

confidence: 99%

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A high frame-rate of cell-based histogram-oriented gradients human detector architecture implemented in field programmable gate arrays

Adiono

Kasan

2023

IJ-AI

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In respect of the accuracy, one of the well-known techniques for human detection is the histogram-oriented gradients (HOG) method. Unfortunately, the HOG feature calculation is highly complex and computationally intensive. Thus, in this research, we aim to achieve a resource-efficient and low-power HOG hardware architecture while maintaining its high frame-rate performance for real-time processing. A hardware architecture for human detection in 2D images using simplified HOG algorithm was introduced in this paper. To increase the frame-rate, we simplify the HOG computation while maintaining the detection quality. In the hardware architecture, we design a cell-based processing method instead of a window-based method. Moreover, 64 parallel and pipeline architectures were used to increase the processing speed. Our pipeline architecture can significantly reduce memory bandwidth and avoid any external memory utilization. an altera field programmable gate arrays (FPGA) E2-115 was employed to evaluate the design. The evaluation results show that our design achieves performance up to 86.51 frame rate per second (Fps) with a relatively low operating frequency (27 MHz). It consumes 48,360 logic elements (LEs) and 4,363 registers. The performance test results reveal that the proposed solution exhibits a trade-off between Fps, clock frequency, the use of registers, and Fps-to-clock ratio.

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Section: Cell Derivates With Edge Neighboring Anti-aliasingmentioning

confidence: 99%

“…Image processing has been widely utilized to detect humans [1], [2]. However, detecting humans in an image is challenging due to their various and wide range of appearance variables [3].…”

Section: Introductionmentioning

confidence: 99%

A high frame-rate of cell-based histogram-oriented gradients human detector architecture implemented in field programmable gate arrays

Adiono

Kasan

2023

IJ-AI

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“…Another technique like the Histogram Oriented Gradients (HOG) actually can be used as a method for face recognition application [12][13][14][15]. Facial recognition is a method for character recognition on faces that are successfully detected.…”

Section: Introductionmentioning

confidence: 99%

Attendance System using Machine Learning-based Face Detection for Meeting Room Application

Muttaqin¹,

Nopendri²,

Mulyana³

2020

IJACSA

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In a modern meeting room, a smart system to make attendance quickly is mandatory. Most of the existing systems perform manual attendance, such as registration and fingerprint. Despite the fingerprint method can reject the Unknown person and give the grant access to the Known person, it will take time to register first a person one-by-one. Moreover, it is possible to create long queues for fingerprint checking before entering the meeting room. Machine learning, along with the Internet of Things (IoT) technology is the best solution; it offers many advantages when applied in the meeting rooms. Generally, the method used is to create a presence by detecting faces. In this paper, we present a facial recognition authentication based on machine learning technology for connection to the meeting rooms. Furthermore, specific website to display the detection result and data storage design testing is developed. The method uses 1) the Dlib library for deep learning purposes, 2) OpenCV for video camera processing, and 3) Face Recognition for Dlib processing. The proposed system allows placing the multiple cameras in a meeting room as needed. However, in this work, we only used one camera as the main system. Tests conducted include identification of one Known person, identification of one Unknown person, identification of two people, and three people. The parameter to be focused is the required time in detecting the number of faces recorded by the camera. The results reveal that the face can be recognized or not recognized, then it will be displayed on the website.

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A Pre-processing-Aided Deep Transfer Learning Model for Human Object Detection in Crowd Scenarios

Hazra,

Khatua,

Saha

2023

Algorithms for Intelligent Systems

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HOG-AdaBoost Implementation for Human Detection Employing FPGA ALTERA DE2-115

Cited by 7 publications

References 10 publications

A high frame-rate of cell-based histogram-oriented gradients human detector architecture implemented in field programmable gate arrays

A high frame-rate of cell-based histogram-oriented gradients human detector architecture implemented in field programmable gate arrays

Attendance System using Machine Learning-based Face Detection for Meeting Room Application

A Pre-processing-Aided Deep Transfer Learning Model for Human Object Detection in Crowd Scenarios

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