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 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.