# Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset

arXiv: https://arxiv.org/abs/2412.06647 · HTML: https://mlsanity.com/paper/2412.06647/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 45 of its 61 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Placed in

- Mixture-of-experts / [Vision mixture-of-experts](https://mlsanity.com/moe/vision-mixture-of-experts/)

## Builds on (most cited first, 30 of 45)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [DETRs with Collaborative Hybrid Assignments Training](https://mlsanity.com/paper/2211.12860/) (2022)
- [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://mlsanity.com/paper/2006.02334/) (2020)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/1903.06530/) (2019)
- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [Rethinking Transformer-based Set Prediction for Object Detection](https://mlsanity.com/paper/2011.10881/) (2020)
- [Efficient DETR: Improving End-to-End Object Detector with Dense Prior](https://mlsanity.com/paper/2104.01318/) (2021)
- [A Large Scale Event-based Detection Dataset for Automotive](https://mlsanity.com/paper/2001.08499/) (2020)
- [End-to-End Object Detection with Adaptive Clustering Transformer](https://mlsanity.com/paper/2011.09315/) (2020)
- [Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity](https://mlsanity.com/paper/2111.14330/) (2021)
- [Deep Directly-Trained Spiking Neural Networks for Object Detection](https://mlsanity.com/paper/2307.11411/) (2023)
- [State Space Models for Event Cameras](https://mlsanity.com/paper/2402.15584/) (2024)
- [Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline](https://mlsanity.com/paper/2309.14611/) (2023)

## Built on by (most cited first, 25 of 25)

- [A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2503.07137/) (2025)
- [Event-based Tiny Object Detection: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2506.23575/) (2025)
- [Adaptive Perception for Unified Visual Multi-modal Object Tracking](https://mlsanity.com/paper/2502.06583/) (2025)
- [Drone Detection with Event Cameras](https://mlsanity.com/paper/2508.04564/) (2025)
- [Adaptive Event Stream Slicing for Open-Vocabulary Event-Based Object Detection via Vision-Language Knowledge Distillation](https://mlsanity.com/paper/2510.00681/) (2025)
- [Sign Language Translation using Frame and Event Stream: Benchmark Dataset and Algorithms](https://mlsanity.com/paper/2503.06484/) (2025)
- [CM3AE: A Unified RGB Frame and Event-Voxel/-Frame Pre-training Framework](https://mlsanity.com/paper/2504.12576/) (2025)
- [ESTR-CoT: Towards Explainable and Accurate Event Stream based Scene Text Recognition with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2507.02200/) (2025)
- [EvRainDrop: HyperGraph-guided Completion for Effective Frame and Event Stream Aggregation](https://mlsanity.com/paper/2511.21439/) (2025)
- [SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation](https://mlsanity.com/paper/2602.19213/) (2026)
- [Human Activity Recognition using RGB-Event based Sensors: A Multi-modal Heat Conduction Model and A Benchmark Dataset](https://mlsanity.com/paper/2504.05830/) (2025)
- [EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception](https://mlsanity.com/paper/2504.16616/) (2025)
- [Dynamic Graph Induced Contour-aware Heat Conduction Network for Event-based Object Detection](https://mlsanity.com/paper/2505.12908/) (2025)
- [ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras](https://mlsanity.com/paper/2607.11303/) (2026)
- [NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices](https://mlsanity.com/paper/2504.03118/) (2025)
- [CETUS: Causal Event-Driven Temporal Modeling With Unified Variable-Rate Scheduling](https://mlsanity.com/paper/2509.13784/) (2025)
- [Temporal Dynamics Enhancer for Directly Trained Spiking Object Detectors](https://mlsanity.com/paper/2512.02447/) (2025)
- [EPRBench: A High-Quality Benchmark Dataset for Event Stream Based Visual Place Recognition](https://mlsanity.com/paper/2602.12919/) (2026)
- [PFM-VEPAR: Prompting Foundation Models for RGB-Event Camera based Pedestrian Attribute Recognition](https://mlsanity.com/paper/2603.19565/) (2026)
- [FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection](https://mlsanity.com/paper/2606.17334/) (2026)
- [Following the Flow: Advection-Consistent Modeling for Event-based Small Object Detection](https://mlsanity.com/paper/2606.22378/) (2026)
- [Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms](https://mlsanity.com/paper/2607.09114/) (2026)
- [SkyEV: RGB-Event UAV detection and tracking dataset and baseline](https://mlsanity.com/paper/2607.18747/) (2026)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)
- [PointEvent: Rethinking Event-based Tiny Object Detection via Serialized Motion Evidence Accumulation](https://mlsanity.com/paper/2609.20066/) (2026)

## Abstract (arXiv)

Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements. Current detectors leverage spiking neural networks, Transformers, or convolutional neural networks as their core architectures, each with its own set of limitations including restricted performance, high computational overhead, or limited local receptive fields. This paper introduces a novel MoE (Mixture of Experts) heat conduction-based object detection algorithm that strikingly balances accuracy and computational efficiency. Initially, we employ a stem network for event data embedding, followed by processing through our innovative MoE-HCO blocks. Each block integrates various expert modules to mimic heat conduction within event streams. Subsequently, an IoU-based query selection module is utilized for efficient token extraction, which is then channeled into a detection head for the final object detection process. Furthermore, we are pleased to introduce EvDET200K, a novel benchmark dataset for event-based object detection. Captured with a high-definition Prophesee EVK4-HD event camera, this dataset encompasses 10 distinct categories, 200,000 bounding boxes, and 10,054 samples, each spanning 2 to 5 seconds. We also provide comprehensive results from over 15 state-of-the-art detectors, offering a solid foundation for future research and comparison. The source code of this paper will be released on: https://github.com/Event-AHU/OpenEvDET