Low-complexity HOG for efficient video saliency

In this paper, we propose a low-complexity histogram of oriented gradients (HOG) implementation for efficient video saliency framework. After showing how original HOG calculations present significant computation bottleneck for visual understanding pipes, we present the optimized HOG flow and algorit...

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Bibliographic Details
Published in2015 IEEE International Conference on Image Processing (ICIP) pp. 3749 - 3752
Main Authors Teahyung Lee, Myung Hwangbo, Alan, Tanfer, Tickoo, Omesh, Iyer, Ravishankar
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2015
Subjects
Algorithm design and analysis
Feature extraction
Histograms
low-complexity HOG
Memory management
Object detection
Real-time systems
video saliency
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Summary:In this paper, we propose a low-complexity histogram of oriented gradients (HOG) implementation for efficient video saliency framework. After showing how original HOG calculations present significant computation bottleneck for visual understanding pipes, we present the optimized HOG flow and algorithm for video saliency framework, which can reduce computational requirements without losing algorithmic performance. Furthermore, simplification for light-weight computations and data-reusable scanning for optimal memory usage are explained for improving system efficiency. Based on our testing and analysis, the proposed HOG implementation optimizes computational complexity and performance while maintaining the video saliency algorithm capability.
DOI:10.1109/ICIP.2015.7351505