# View-invariant Deep Architecture for Human Action Recognition using late fusion

arXiv: https://arxiv.org/abs/1912.03632 · HTML: https://mlsanity.com/paper/1912.03632/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

## Built on by (most cited first, 15 of 15)

- [Learning Rich Features for Gait Recognition by Integrating Skeletons and Silhouettes](https://mlsanity.com/paper/2110.13408/) (2021)
- [A Comparative Analysis of Decision-Level Fusion for Multimodal Driver Behaviour Understanding](https://mlsanity.com/paper/2204.04734/) (2022)
- [Modality Mixer for Multi-modal Action Recognition](https://mlsanity.com/paper/2208.11314/) (2022)
- [H-MoRe: Learning Human-centric Motion Representation for Action Analysis](https://mlsanity.com/paper/2504.10676/) (2025)
- [Vision-Language Models Assisted Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2409.14109/) (2024)
- [Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification](https://mlsanity.com/paper/2106.13929/) (2021)
- [ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization](https://mlsanity.com/paper/2208.09414/) (2022)
- [Kinematic-aware Hierarchical Attention Network for Human Pose Estimation in Videos](https://mlsanity.com/paper/2211.15868/) (2022)
- [Unsupervised View-Invariant Human Posture Representation](https://mlsanity.com/paper/2109.08730/) (2021)
- [Transformers in Action Recognition: A Review on Temporal Modeling](https://mlsanity.com/paper/2302.01921/) (2022)
- [TransNet: A Transfer Learning-Based Network for Human Action Recognition](https://mlsanity.com/paper/2309.06951/) (2023)
- [An Integrated Framework for Multi-Granular Explanation of Video Summarization](https://mlsanity.com/paper/2405.10082/) (2024)
- [Reasonable Anomaly Detection in Long Sequences](https://mlsanity.com/paper/2309.03401/) (2023)
- [Modality Mixer Exploiting Complementary Information for Multi-modal Action Recognition](https://mlsanity.com/paper/2311.12344/) (2023)
- [An Improved Graph Pooling Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2404.16359/) (2024)

## Abstract (arXiv)

Human action Recognition for unknown views is a challenging task. We propose a view-invariant deep human action recognition framework, which is a novel integration of two important action cues: motion and shape temporal dynamics (STD). The motion stream encapsulates the motion content of action as RGB Dynamic Images (RGB-DIs) which are processed by the fine-tuned InceptionV3 model. The STD stream learns long-term view-invariant shape dynamics of action using human pose model (HPM) based view-invariant features mined from structural similarity index matrix (SSIM) based key depth human pose frames. To predict the score of the test sample, three types of late fusion (maximum, average and product) techniques are applied on individual stream scores. To validate the performance of the proposed novel framework the experiments are performed using both cross subject and cross-view validation schemes on three publically available benchmarks- NUCLA multi-view dataset, UWA3D-II Activity dataset and NTU RGB-D Activity dataset. Our algorithm outperforms with existing state-of-the-arts significantly that is reported in terms of accuracy, receiver operating characteristic (ROC) curve and area under the curve (AUC).