# Gradient Boundary Histograms for Action Recognition

arXiv: https://arxiv.org/abs/1412.1194 · HTML: https://mlsanity.com/paper/1412.1194/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2014.

## Built on by (most cited first, 1 of 1)

- [Efficient data-driven encoding of scene motion using Eccentricity](https://mlsanity.com/paper/2103.02743/) (2021)

## Abstract (arXiv)

This paper introduces a high efficient local spatiotemporal descriptor, called gradient boundary histograms (GBH). The proposed GBH descriptor is built on simple spatio-temporal gradients, which are fast to compute. We demonstrate that it can better represent local structure and motion than other gradient-based descriptors, and significantly outperforms them on large realistic datasets. A comprehensive evaluation shows that the recognition accuracy is preserved while the spatial resolution is greatly reduced, which yields both high efficiency and low memory usage.