# Human Action Recognition without Human

arXiv: https://arxiv.org/abs/1608.07876 · HTML: https://mlsanity.com/paper/1608.07876/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2016.

## Built on by (most cited first, 17 of 17)

- [Motion-aware Contrastive Video Representation Learning via Foreground-background Merging](https://mlsanity.com/paper/2109.15130/) (2021)
- [A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information](https://mlsanity.com/paper/2206.02846/) (2022)
- [Just Add π! Pose Induced Video Transformers for Understanding Activities of Daily Living](https://mlsanity.com/paper/2311.18840/) (2023)
- [Is Appearance Free Action Recognition Possible?](https://mlsanity.com/paper/2207.06261/) (2022)
- [Dual Contrastive Learning for Spatio-temporal Representation](https://mlsanity.com/paper/2207.05340/) (2022)
- [CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes](https://mlsanity.com/paper/2404.01299/) (2024)
- [Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks](https://mlsanity.com/paper/2211.01783/) (2022)
- [Can masking background and object reduce static bias for zero-shot action recognition?](https://mlsanity.com/paper/2501.12681/) (2025)
- [The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition](https://mlsanity.com/paper/2502.21201/) (2025)
- [Ultra-high Resolution Image Segmentation via Locality-aware Context Fusion and Alternating Local Enhancement](https://mlsanity.com/paper/2109.02580/) (2021)
- [S3Aug: Segmentation, Sampling, and Shift for Action Recognition](https://mlsanity.com/paper/2310.14556/) (2023)
- [Deep set conditioned latent representations for action recognition](https://mlsanity.com/paper/2212.11030/) (2022)
- [Identifying Ethical Biases in Action Recognition Models](https://mlsanity.com/paper/2604.17971/) (2026)
- [Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach](https://mlsanity.com/paper/2105.08253/) (2021)
- [Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition](https://mlsanity.com/paper/2207.13306/) (2022)
- [MoExDA: Domain Adaptation for Edge-based Action Recognition](https://mlsanity.com/paper/2508.02981/) (2025)
- [Recognition of Daily Activities through Multi-Modal Deep Learning: A Video, Pose, and Object-Aware Approach for Ambient Assisted Living](https://mlsanity.com/paper/2603.04509/) (2026)

## Abstract (arXiv)

The objective of this paper is to evaluate "human action recognition without human". Motion representation is frequently discussed in human action recognition. We have examined several sophisticated options, such as dense trajectories (DT) and the two-stream convolutional neural network (CNN). However, some features from the background could be too strong, as shown in some recent studies on human action recognition. Therefore, we considered whether a background sequence alone can classify human actions in current large-scale action datasets (e.g., UCF101). In this paper, we propose a novel concept for human action analysis that is named "human action recognition without human". An experiment clearly shows the effect of a background sequence for understanding an action label.