HOG Feature Extractor Hardware Accelerator for Real-Time Pedestrian Detection

Hemmati, Maryam; Biglari-Abhari, Morteza; Berber, Stevan; Niar, Smaïl

doi:10.1109/dsd.2014.60

Cited by 29 publications

(11 citation statements)

References 12 publications

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“…The literature also contains reports on HOG implementations in FPGAs, especially for pedestrian detection [18][19][20][21][22][23][24][25][26][27][28][29]. For us, it is interesting to note that they also include some previously introduced simplifications of HOG calculation, which are mostly implementation-related simplifications, rather than intrinsic algorithm changes.…”

Section: Related Workmentioning

confidence: 99%

“…For us, it is interesting to note that they also include some previously introduced simplifications of HOG calculation, which are mostly implementation-related simplifications, rather than intrinsic algorithm changes. Such changes include the usage of lookup tables (LUTs) for obtaining the square root, replacement of several multiplications by shifting operations, as well as using for normalization the closest power-of-two number [21,22]. In [18] the authors present in detail the implementation of HOG and SVM, for person detection, on an FPGA.…”

Section: Related Workmentioning

confidence: 99%

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A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

Ilas

2018

Computers

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In this paper we introduce a new histogram computation method to be used within the histogram of oriented gradients (HOG) algorithm. The new method replaces the arctangent with the slope computation and the classical magnitude allocation based on interpolation with a simpler algorithm. The new method allows a more efficient implementation of HOG in general, and particularly in field-programmable gate arrays (FPGAs), by considerably reducing the area (thus increasing the level of parallelism), while maintaining very close classification accuracy compared to the original algorithm. Thus, the new method is attractive for many applications, including car detection and classification.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

Ilas

2018

Computers

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“…In the area of FPGA emulations, Kadota et al [12] proposed a simplified HOG calculation and designed the HOG hardware with Altera Stratix II FPGA. Hemmati et al [13] applied Xilinx Zynq-7000 FPGA to design the HOG calculation speed up to 60 FPS under 1920 × 1080 HDTV resolutions. Although the speed in above research could reach real-time, it was merely the calculation result of a single HOG, but ignored the connection and the computing time of the rest functional modules required for data input/output and applied them to the entire set of human detection system.…”

Section: Introductionmentioning

confidence: 99%

An FPGA-Based HOG Accelerator with HW/SW Co-Design for Human Detection and Its Application to Crowd Density Estimation

Huang¹,

Lin²,

Hsiao³

2019

JSEA

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Human detection is important in many applications and has attracted significant attention over the last decade. The Histograms of Oriented Gradients (HOG) as effective local descriptors are used with binary sliding window mechanism to achieve good detection performance. However, the computation of HOG under such framework is about billion times and the pure software implementation for HOG computation is hard to meet the real-time requirement. This study proposes a hardware architecture called One-HOG accelerator operated on FPGA of Xilinx Spartan-6 LX-150T that provides an efficient way to compute HOG such that an embedded real-time platform of HW/SW co-design for application to crowd estimation and analysis is achieved. The One-HOG accelerator mainly consists of gradient module and histogram module. The gradient module is for computing gradient magnitude and orientation; histogram module is for generating a 36-D HOG feature vector. In addition to hardware realization, a new method called Histograms-of-Oriented-Gradients AdaBoost Long-Feature-Vector (HOG-AdaBoost-LFV) human classifier is proposed to significantly decrease the number of times to compute the HOG without sacrificing detection performance. The experiment results from three static image and four video datasets demonstrate that the proposed SW/HW (software/hardware) co-design system is 13.14 times faster than the pure software computation of Dalal algorithm.

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“…To date, there are many papers on FPGA-based HOG that implemented in Xillix FPGA for high-speed and highaccuracy human detection systems [6][7][8][9][10]. In previous work [11], we have applied the FPGA-based human recognition in an image, we employed ALTERA DE2-115.…”

Section: Introductionmentioning

confidence: 99%

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

Adiono¹,

Prakoso²,

Putratama³

et al. 2018

ijacsa

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Human detection system using Histogram of Oriented Gradients (HOG) feature and AdaBoost classifier (HOG-AdaBoost) in FPGA ALTERA DE2-115 are presented in this paper. This work is expanded version from our previous study. This paper discusses 1) the HOG performance in detecting human from a passive images with other point-of-views (30 deg., 40 deg., 50 deg., 60 deg. and up to 70 deg.); 2) FPS test with various image sizes (320 x 240, 640 x 480, 800 x 600, and 1280 x 1024); 3) re-measurement the FPGA's power consumption and 4) simulate the architecture in RTL. We used three databases as a parameter for test purpose, i.e. INRIA, MIT, and Daimler.

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HOG Feature Extractor Hardware Accelerator for Real-Time Pedestrian Detection

Cited by 29 publications

References 12 publications

A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

An FPGA-Based HOG Accelerator with HW/SW Co-Design for Human Detection and Its Application to Crowd Density Estimation

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

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