# Temporal Binary Representation for Event-Based Action Recognition

arXiv: https://arxiv.org/abs/2010.08946 · HTML: https://mlsanity.com/paper/2010.08946/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2020.

## Built on by (most cited first, 30 of 51)

- [N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras](https://mlsanity.com/paper/2112.01041/) (2021)
- [GET: Group Event Transformer for Event-Based Vision](https://mlsanity.com/paper/2310.02642/) (2023)
- [E2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition](https://mlsanity.com/paper/2112.03596/) (2021)
- [Event Transformer. A sparse-aware solution for efficient event data processing](https://mlsanity.com/paper/2204.03355/) (2022)
- [ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and More](https://mlsanity.com/paper/2403.12534/) (2024)
- [Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models](https://mlsanity.com/paper/2404.18508/) (2024)
- [SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition](https://mlsanity.com/paper/2310.07189/) (2023)
- [Efficient recurrent architectures through activity sparsity and sparse back-propagation through time](https://mlsanity.com/paper/2206.06178/) (2022)
- [Hypergraph-based Multi-View Action Recognition using Event Cameras](https://mlsanity.com/paper/2403.19316/) (2024)
- [Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba](https://mlsanity.com/paper/2405.06116/) (2024)
- [Neuromorphic Event-based Facial Expression Recognition](https://mlsanity.com/paper/2304.06351/) (2023)
- [FRED: The Florence RGB-Event Drone Dataset](https://mlsanity.com/paper/2506.05163/) (2025)
- [DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation](https://mlsanity.com/paper/2103.12768/) (2021)
- [Neuromorphic Drone Detection: an Event-RGB Multimodal Approach](https://mlsanity.com/paper/2409.16099/) (2024)
- [Event Transformer+. A multi-purpose solution for efficient event data processing](https://mlsanity.com/paper/2211.12222/) (2022)
- [TTPOINT: A Tensorized Point Cloud Network for Lightweight Action Recognition with Event Cameras](https://mlsanity.com/paper/2308.09993/) (2023)
- [EventTransAct: A video transformer-based framework for Event-camera based action recognition](https://mlsanity.com/paper/2308.13711/) (2023)
- [A Comprehensive Review of Few-shot Action Recognition](https://mlsanity.com/paper/2407.14744/) (2024)
- [SpikMamba: When SNN meets Mamba in Event-based Human Action Recognition](https://mlsanity.com/paper/2410.16746/) (2024)
- [Neuromorphic Face Analysis: a Survey](https://mlsanity.com/paper/2402.11631/) (2024)
- [Drone Detection with Event Cameras](https://mlsanity.com/paper/2508.04564/) (2025)
- [ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data](https://mlsanity.com/paper/2402.01393/) (2024)
- [Neuromorphic Facial Analysis with Cross-Modal Supervision](https://mlsanity.com/paper/2409.10213/) (2024)
- [FAPNet: An Effective Frequency Adaptive Point-based Eye Tracker](https://mlsanity.com/paper/2406.03177/) (2024)
- [Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation](https://mlsanity.com/paper/2504.06748/) (2025)
- [Compute-in-Memory Implementation of State Space Models for Event Sequence Processing](https://mlsanity.com/paper/2511.13912/) (2025)
- [Unleashing the Power of CNN and Transformer for Balanced RGB-Event Video Recognition](https://mlsanity.com/paper/2312.11128/) (2023)
- [Neuromorphic Valence and Arousal Estimation](https://mlsanity.com/paper/2401.16058/) (2024)
- [PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining](https://mlsanity.com/paper/2505.05307/) (2025)
- [EventSleep: Sleep Activity Recognition with Event Cameras](https://mlsanity.com/paper/2404.01801/) (2024)

## Abstract (arXiv)

In this paper we present an event aggregation strategy to convert the output of an event camera into frames processable by traditional Computer Vision algorithms. The proposed method first generates sequences of intermediate binary representations, which are then losslessly transformed into a compact format by simply applying a binary-to-decimal conversion. This strategy allows us to encode temporal information directly into pixel values, which are then interpreted by deep learning models. We apply our strategy, called Temporal Binary Representation, to the task of Gesture Recognition, obtaining state of the art results on the popular DVS128 Gesture Dataset. To underline the effectiveness of the proposed method compared to existing ones, we also collect an extension of the dataset under more challenging conditions on which to perform experiments.