# Dynamic Vision Sensors for Human Activity Recognition

arXiv: https://arxiv.org/abs/1803.04667 · HTML: https://mlsanity.com/paper/1803.04667/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors](https://mlsanity.com/paper/2211.09648/) (2022)
- [Efficient Human Pose Estimation via 3D Event Point Cloud](https://mlsanity.com/paper/2206.04511/) (2022)
- [V2CE: Video to Continuous Events Simulator](https://mlsanity.com/paper/2309.08891/) (2023)
- [Classification of Primitive Manufacturing Tasks from Filtered Event Data](https://mlsanity.com/paper/2303.09558/) (2023)
- [Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation](https://mlsanity.com/paper/2104.05145/) (2021)
- [Lossy Event Compression: From Event Stream Distortion to Task Performance](https://mlsanity.com/paper/2608.28429/) (2026)

## Abstract (arXiv)

Unlike conventional cameras which capture video at a fixed frame rate, Dynamic Vision Sensors (DVS) record only changes in pixel intensity values. The output of DVS is simply a stream of discrete ON/OFF events based on the polarity of change in its pixel values. DVS has many attractive features such as low power consumption, high temporal resolution, high dynamic range and fewer storage requirements. All these make DVS a very promising camera for potential applications in wearable platforms where power consumption is a major concern. In this paper, we explore the feasibility of using DVS for Human Activity Recognition (HAR). We propose to use the various slices (such as x-y, x-t, and y-t) of the DVS video as a feature map for HAR and denote them as Motion Maps. We show that fusing motion maps with Motion Boundary Histogram (MBH) give good performance on the benchmark DVS dataset as well as on a real DVS gesture dataset collected by us. Interestingly, the performance of DVS is comparable to that of conventional videos although DVS captures only sparse motion information.