An Energy-Efficient Hardware Implementation of HOG-Based Object Detection at 1080HD 60 fps with Multi-Scale Support
Name
suleiman_jsps_2015.pdf
Size
2.08 MB
Format
Adobe PDF
Checksum (MD5)
64a636263e99329f2f1e475b1956b182
Author(s)
Sze, Vivienne
Suleiman, Amr AbdulZahir
Date Issued
December 2015
Journal
Journal of Signal Processing Systems
Publisher
Springer-Verlag
Citation
Suleiman, Amr, and Vivienne Sze. "An Energy-Efficient Hardware Implementation of HOG-Based Object Detection at 1080HD 60 fps with Multi-Scale Support." Journal of Signal Processing Systems (December 2015).
Version
Author's final manuscript
Abstract
A real-time and energy-efficient multi-scale object detector hardware implementation is presented in this paper. Detection is done using Histogram of Oriented Gradients (HOG) features and Support Vector Machine (SVM) classification. Multi-scale detection is essential for robust and practical applications to detect objects of different sizes. Parallel detectors with balanced workload are used to increase the throughput, enabling voltage scaling and energy consumption reduction. Image pre-processing is also introduced to further reduce power and area costs of the image scales generation. This design can operate on high definition 1080HD video at 60 fps in real-time with a clock rate of 270 MHz, and consumes 45.3 mW (0.36 nJ/pixel) based on post-layout simulations. The ASIC has an area of 490 kgates and 0.538 Mbit on-chip memory in a 45 nm SOI CMOS process.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11265-015-1080-7
