Abstract
The field of computer vision is gaining more importance because of its varied applications in numerous domains. Object detection and recognition are the most widely used applications. Intelligent and flexible Field Programmable Gate Array—FPGA architectures are replacing the current CPU-based hardware architectures for implementing image processing algorithms. We propose a hardware implementation of a widely used algorithm in object detection—Pyramidal Histogram of Oriented Gradients – PHOG. We have used Vivado High-Level Synthesis—HLS software to implement the PHOG algorithm, through which we can code in the algorithmic level using C/C+ +. In this research work, we explain the PHOG block’s implementation and all other underlying blocks. We also discuss the optimizations and modifications introduced in the algorithm and compare the results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bosch A, Zisserman A, Munoz X (2007) Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM international conference on Image and video retrieval
Saïdani A, Echi AK (2014) Pyramid histogram of oriented gradient for machine-printed/handwritten and Arabic/Latin word discrimination. In: 2014 6th international conference of soft computing and pattern recognition (SoCPaR). IEEEs
Tan ZR, Tian S, Tan CL (2014), Using pyramid of histogram of oriented gradients on natural scene text recognition. In: 2014 IEEE international conference on image processing (ICIP), Paris, France, pp 2629–2633. https://doi.org/10.1109/ICIP.2014.7025532
Bai Y et al (2009) A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In: 2009 16th IEEE international conference on image processing (ICIP). IEEE
Zhang B et al (2010) Historic Chinese architectures image retrieval by SVM and pyramid histogram of oriented gradients features. Int J Soft Comput 5(2):19–28
Lee SE, Min K, Suh T (2013) Accelerating histograms of oriented gradients descriptor extraction for pedestrian recognition. Comput Electr Eng 39(4):1043–1048
Mizuno K et al Architectural study of HOG feature extraction processor for real-time object detection. In: 2012 IEEE workshop on signal processing systems. IEEE
Hemmati M et al (2014) HOG feature extractor hardware accelerator for real-time pedestrian detection. In: 2014 17th Euromicro conference on digital system design. IEEE
Nuno-Maganda MA, Arias-Estrada MO (2005) Real-time FPGA-based architecture for bicubic interpolation: an application for digital image scaling. In: 2005 international conference on reconfigurable computing and FPGAs (ReConFig’05). IEEE
Li Y, Su G (2015) Simplified histograms of oriented gradient features extraction algorithm for the hardware implementation. In: 2015 international conference on computers, communications, and systems (ICCCS). IEEE
Kadota R et al (2009) Hardware architecture for HOG feature extraction. In: 2009 fifth international conference on intelligent information hiding and multimedia signal processing. IEEE
Negi K et al (2011) Deep pipelined one-chip FPGA implementation of a real-time image-based human detection algorithm. In: 2011 international conference on field-programmable technology. IEEE
Cao TP, Deng G (2008) Real-time vision-based stop sign detection system on FPGA. In: 2008 digital image computing: techniques and applications. IEEE
Georgopoulos K et al (2016) An evaluation of vivado HLS for efficient system design. In: 2016 international symposium ELMAR, Zadar, pp 195–199. https://doi.org/10.1109/ELMAR.2016.7731785
Feist T (2012) Vivado design suite. **linx Inc., San Jose, CA, USA, White Paper 5, 2012, p 30
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1, pp 886–893
Lazebnik, Svetlana, Cordelia Schmid, and Jean Ponce. “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories.“ 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). Vol. 2. IEEE, 2006
Burkardt J (2020) TIFF Files, Accessed on: Aug. 21, 2020. [Online]. Available http://people.math.sc.edu/Burkardt/data/tif/tif.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Purushothaman, P., Srihari, S., Raj, A.N.J., Bhaskar, M. (2022). Hardware Implementation of Pyramidal Histogram of Oriented Gradients. In: Gupta, G., Wang, L., Yadav, A., Rana, P., Wang, Z. (eds) Proceedings of Academia-Industry Consortium for Data Science. Advances in Intelligent Systems and Computing, vol 1411. Springer, Singapore. https://doi.org/10.1007/978-981-16-6887-6_6
Download citation
DOI: https://doi.org/10.1007/978-981-16-6887-6_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6886-9
Online ISBN: 978-981-16-6887-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)