# A Hybrid Framework for Action Recognition in Low-Quality Video Sequences

arXiv: https://arxiv.org/abs/1903.04090 · HTML: https://mlsanity.com/paper/1903.04090/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [Detecting Activities of Daily Living in Egocentric Video to Contextualize Hand Use at Home in Outpatient Neurorehabilitation Settings](https://mlsanity.com/paper/2412.10846/) (2024)
- [Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System](https://mlsanity.com/paper/2107.12744/) (2021)

## Abstract (arXiv)

Vision-based activity recognition is essential for security, monitoring and surveillance applications. Further, real-time analysis having low-quality video and contain less information about surrounding due to poor illumination, and occlusions. Therefore, it needs a more robust and integrated model for low quality and night security operations. In this context, we proposed a hybrid model for illumination invariant human activity recognition based on sub-image histogram equalization enhancement and k-key pose human silhouettes. This feature vector gives good average recognition accuracy on three low exposure video sequences subset of original actions video datasets. Finally, the performance of the proposed approach is tested over three manually downgraded low qualities Weizmann action, KTH, and Ballet Movement dataset. This model outperformed on low exposure videos over existing technique and achieved comparable classification accuracy to similar state-of-the-art methods.