# Action Recognition: From Static Datasets to Moving Robots

arXiv: https://arxiv.org/abs/1701.04925 · HTML: https://mlsanity.com/paper/1701.04925/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2017.

## Built on by (most cited first, 9 of 9)

- [Home Action Genome: Cooperative Compositional Action Understanding](https://mlsanity.com/paper/2105.05226/) (2021)
- [Advancing Video Anomaly Detection: A Concise Review and a New Dataset](https://mlsanity.com/paper/2402.04857/) (2024)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging](https://mlsanity.com/paper/2101.03478/) (2021)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning](https://mlsanity.com/paper/2303.01589/) (2023)
- [MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation](https://mlsanity.com/paper/2311.08393/) (2023)
- [From Detection to Action Recognition: An Edge-Based Pipeline for Robot Human Perception](https://mlsanity.com/paper/2312.03477/) (2023)
- [RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems](https://mlsanity.com/paper/2205.00364/) (2022)

## Abstract (arXiv)

Deep learning models have achieved state-of-the- art performance in recognizing human activities, but often rely on utilizing background cues present in typical computer vision datasets that predominantly have a stationary camera. If these models are to be employed by autonomous robots in real world environments, they must be adapted to perform independently of background cues and camera motion effects. To address these challenges, we propose a new method that firstly generates generic action region proposals with good potential to locate one human action in unconstrained videos regardless of camera motion and then uses action proposals to extract and classify effective shape and motion features by a ConvNet framework. In a range of experiments, we demonstrate that by actively proposing action regions during both training and testing, state-of-the-art or better performance is achieved on benchmarks. We show the outperformance of our approach compared to the state-of-the-art in two new datasets; one emphasizes on irrelevant background, the other highlights the camera motion. We also validate our action recognition method in an abnormal behavior detection scenario to improve workplace safety. The results verify a higher success rate for our method due to the ability of our system to recognize human actions regardless of environment and camera motion.