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Home > All Category Datasets > Computer Vision Datasets > 16,270 Videos - Passenger Lose Behavior Data

16,270 Videos - Passenger Lose Behavior Data

Passenger lost
Visible light
Infrared
In-car scene

16,270 Videos - Passenger Lose Behavior Data, covering loss events such as item loss and pet loss. It can be used for tasks including lost‑event detection.

Paid Datasets
This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
SpecificationsSpecifications
Data size
16,270 videos, covering loss events including item loss, pet loss, and other related loss incidents.
Data resolution
no less than 1,080P
Data format
the videos data format is commonly used in formats such as .mp4
Sample Sample
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What types of computer vision applications can Nexdata’s datasets support?

Nexdata’s computer vision datasets support a wide range of AI applications, including image classification, object detection, image segmentation, facial and human-related recognition, scene understanding, autonomous driving, and other visual perception tasks. Depending on the dataset, data may include images, videos, bounding boxes, polygons, keypoints, segmentation masks, text annotations, and other structured labels.

Can Nexdata customize Computer Vision datasets based on our specific requirements?

Yes. If our off-the-shelf Computer Vision datasets do not fully meet your requirements, Nexdata provides flexible custom data collection, annotation, and curation services. We can customize data based on your target objects, environments, scenarios, camera specifications, geographic locations, data volume, annotation formats, and quality standards to support specific model training and evaluation needs.

How does Nexdata ensure the quality and scalability of its Computer Vision datasets?

Nexdata applies multi-stage quality control throughout data collection, annotation, validation, and delivery. Depending on project requirements, we can implement customized annotation guidelines, multi-level reviews, consistency checks, and quality sampling to ensure dataset accuracy and consistency. Our data collection and processing capabilities can also be scaled to support large-volume Computer Vision projects.

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