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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.20446 |
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| _version_ | 1866909273755222016 |
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| author | Shair, Zaid A. El Rawashdeh, Samir A. |
| author_facet | Shair, Zaid A. El Rawashdeh, Samir A. |
| contents | In this data article, we introduce the Multi-Modal Event-based Vehicle Detection and Tracking (MEVDT) dataset. This dataset provides a synchronized stream of event data and grayscale images of traffic scenes, captured using the Dynamic and Active-Pixel Vision Sensor (DAVIS) 240c hybrid event-based camera. MEVDT comprises 63 multi-modal sequences with approximately 13k images, 5M events, 10k object labels, and 85 unique object tracking trajectories. Additionally, MEVDT includes manually annotated ground truth labels $\unicode{x2014}$ consisting of object classifications, pixel-precise bounding boxes, and unique object IDs $\unicode{x2014}$ which are provided at a labeling frequency of 24 Hz. Designed to advance the research in the domain of event-based vision, MEVDT aims to address the critical need for high-quality, real-world annotated datasets that enable the development and evaluation of object detection and tracking algorithms in automotive environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20446 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset Shair, Zaid A. El Rawashdeh, Samir A. Computer Vision and Pattern Recognition Databases In this data article, we introduce the Multi-Modal Event-based Vehicle Detection and Tracking (MEVDT) dataset. This dataset provides a synchronized stream of event data and grayscale images of traffic scenes, captured using the Dynamic and Active-Pixel Vision Sensor (DAVIS) 240c hybrid event-based camera. MEVDT comprises 63 multi-modal sequences with approximately 13k images, 5M events, 10k object labels, and 85 unique object tracking trajectories. Additionally, MEVDT includes manually annotated ground truth labels $\unicode{x2014}$ consisting of object classifications, pixel-precise bounding boxes, and unique object IDs $\unicode{x2014}$ which are provided at a labeling frequency of 24 Hz. Designed to advance the research in the domain of event-based vision, MEVDT aims to address the critical need for high-quality, real-world annotated datasets that enable the development and evaluation of object detection and tracking algorithms in automotive environments. |
| title | MEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset |
| topic | Computer Vision and Pattern Recognition Databases |
| url | https://arxiv.org/abs/2407.20446 |