MEVDT: Multi-modal event-based vehicle detection and tracking datasetDeep Blue Data
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 c...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924011673 |
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| author | Zaid A. El Shair Samir A. Rawashdeh |
| author_facet | Zaid A. El Shair Samir A. Rawashdeh |
| author_sort | Zaid A. El Shair |
| collection | DOAJ |
| description | 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 — consisting of object classifications, pixel-precise bounding boxes, and unique object IDs — 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 | Article |
| id | doaj-art-9b3883d8ed98437b8e9e911658cef92e |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-9b3883d8ed98437b8e9e911658cef92e2025-01-31T05:11:29ZengElsevierData in Brief2352-34092025-02-0158111205MEVDT: Multi-modal event-based vehicle detection and tracking datasetDeep Blue DataZaid A. El Shair0Samir A. Rawashdeh1Corresponding author.; Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, 48128 MI, USADepartment of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, 48128 MI, USAIn 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 — consisting of object classifications, pixel-precise bounding boxes, and unique object IDs — 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.http://www.sciencedirect.com/science/article/pii/S2352340924011673Event-based visionObject detectionObject trackingMultimodalComputer vision |
| spellingShingle | Zaid A. El Shair Samir A. Rawashdeh MEVDT: Multi-modal event-based vehicle detection and tracking datasetDeep Blue Data Data in Brief Event-based vision Object detection Object tracking Multimodal Computer vision |
| title | MEVDT: Multi-modal event-based vehicle detection and tracking datasetDeep Blue Data |
| title_full | MEVDT: Multi-modal event-based vehicle detection and tracking datasetDeep Blue Data |
| title_fullStr | MEVDT: Multi-modal event-based vehicle detection and tracking datasetDeep Blue Data |
| title_full_unstemmed | MEVDT: Multi-modal event-based vehicle detection and tracking datasetDeep Blue Data |
| title_short | MEVDT: Multi-modal event-based vehicle detection and tracking datasetDeep Blue Data |
| title_sort | mevdt multi modal event based vehicle detection and tracking datasetdeep blue data |
| topic | Event-based vision Object detection Object tracking Multimodal Computer vision |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924011673 |
| work_keys_str_mv | AT zaidaelshair mevdtmultimodaleventbasedvehicledetectionandtrackingdatasetdeepbluedata AT samirarawashdeh mevdtmultimodaleventbasedvehicledetectionandtrackingdatasetdeepbluedata |