A Large Scale Event-based Detection Dataset for Automotive

2020/01/23 by Pierre de Tournemire, de Tournemire, Pierre, Davide Nitti +9 · 29 citations
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #CCD and CMOS Imaging Sensors #Computer Vision and Pattern Recognition (cs.CV) #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Robotics (cs.RO) #cs.CV #cs.LG #cs.RO #eess.IV #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2001.08499

8 pages, 29 images

openalex publication_date 2020/01/23 · arxiv created 2020/01/31 · arxiv updated 2020/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We introduce the first very large detection dataset for event cameras. The dataset is composed of more than 39 hours of automotive recordings acquired with a 304x240 ATIS sensor. It contains open roads and very diverse driving scenarios, ranging from urban, highway, suburbs and countryside scenes, as well as different weather and illumination conditions. Manual bounding box annotations of cars and pedestrians contained in the recordings are also provided at a frequency between 1 and 4Hz, yielding more than 255,000 labels in total. We believe that the availability of a labeled dataset of this size will contribute to major advances in event-based vision tasks such as object detection and classification. We also expect benefits in other tasks such as optical flow, structure from motion and tracking, where for example, the large amount of data can be leveraged by self-supervised learning methods.

Citations

• A 128×128 120 dB 15 μs Latency Asynchronous Temporal Contrast Vision Sensor
• The Pascal Visual Object Classes (VOC) Challenge
• Gradient-based learning applied to document recognition
• ImageNet classification with deep convolutional neural networks

Cited by

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• PEOD: A Pixel-Aligned Event-RGB Benchmark for Object Detection under Challenging Conditions
• Spiking Patches: Asynchronous, Sparse, and Efficient Tokens for Event Cameras
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• SpikePool: Event-driven Spiking Transformer with Pooling Attention
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• TESPEC: Temporally-Enhanced Self-Supervised Pretraining for Event Cameras
• Event-based Graph Representation with Spatial and Motion Vectors for Asynchronous Object Detection
• WD-DETR: Wavelet Denoising-Enhanced Real-Time Object Detection Transformer for Robot Perception with Event Cameras
• Learning to Detect Objects with a 1 Megapixel Event Camera
• Event-based Tiny Object Detection: A Benchmark Dataset and Baseline
• From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications
• Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection
• Probabilistic Online Event Downsampling
• Maximizing Asynchronicity in Event-based Neural Networks
• Self-Supervised Event Representations: Towards Accurate, Real-Time Perception on SoC FPGAs
• Hybrid Spiking Vision Transformer for Object Detection with Event Cameras
• Cooking beyond Frames: A Stereo Event Camera Dataset in the Kitchen
• EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception
• EMF: Event Meta Formers for Event-based Real-time Traffic Object Detection
• Event-based Civil Infrastructure Visual Defect Detection: ev-CIVIL Dataset and Benchmark

Related

• Learning to Detect Objects with a 1 Megapixel Event Camera
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