object detection
We build an original dataset of thermal videos and images that simulate illegal movements around the border and in protected areas and are designed for training machines and deep learning models. The videos are recorded in areas around the forest, at night, in different weather conditions – in the clear weather, in the rain, and in the fog, and with people in different body positions (upright, hunched) and movement speeds (regu- lar walking, running) at different ranges from the camera.
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CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed.
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474 Views
We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM. It comprises RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files)
It aims at People detection, in (mostly) indoor and outdoor environments. People in the field of view can be standing, but also lying on the ground as after a fall.
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406 Views
The benchmark dataset are consisted of 2,413 three-channel RGB images obtained from Google Earth satellite images and AID dataset.
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582 Views
Emergency managers of today grapple with post-hurricane damage assessment that is often labor-intensive, slow,costly, and error-prone. As an important first step towards addressing the challenge, this paper presents the development of benchmark datasets to enable the automatic detection ofdamaged buildings from post-hurricane remote sensing imagerytaken from both airborne and satellite sensors. Our work has two major contributions: (1) we propose a scalable framework to create benchmark datasets of hurricane-damaged buildings
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