The dataset consists of 751 videos, each containing the performance one of the handball actions out of 7 categories (passing, shooting, jump-shot, dribbling, running, crossing, defence). The videos were manually extracted from longer videos recorded in handball practice sessions.
The directory scenes/ contains the videos in mp4 format with actions of interest performed in context of other players present in the scene. The files are arranged in subdirectories according to the action class of the action of interest. The directory actions/ contains the videos of performances of actions by single players isolated from the videos in scenes directory. The files are arranged in subdirectories according to the performed action class. Files are named so that the beginning of the name matches the original video from which the action is extracted. The directory player_detections/ contains the object detections for each frame in the videos.
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SDU-Haier-AQD (Shandong University-Haier-Appearance Quality Detection) is an image dataset jointly constructed by Shandong University and Haier, which contains a various of air conditioner external unit image collected during actual detection process.The Appearance Quality Detection (AQD) dataset is consisted of 10449 images, and the samples in the dataset are collected on the actual industrial production line of air conditioner.
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For the task of detecting casualties and persons in search and rescue scenarios in drone images and videos, our database called SARD was built. The actors in the footage have simulate exhausted and injured persons as well as "classic" types of movement of people in nature, such as running, walking, standing, sitting, or lying down. Since different types of terrain and backgrounds determine possible events and scenarios in captured images and videos, the shots include persons on macadam roads, in quarries, low and high grass, forest shade, and the like.
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Networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spec- troscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments.
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Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.
Many of the investigator working on disease detection in Basil leaves where the following diseases occur
1) Gray Mold
2) Basal Root Rot, Damping Off
3) Fusarium Wilt and Crown Rot
Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.
Many of the investigator working on disease detection in Basil leaves where the following diseases occur
1) Gray Mold
2) Basal Root Rot, Damping Off
3) Fusarium Wilt and Crown Rot
4) Leaf Spot
5) Downy Mildew
The Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo, TensorFlow, OpenCV, deep learning, CNN
I had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.
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BTH Trucks in Aerial Images Dataset contains videos of 17 flights across two industrial harbors' parking spaces over two years.
If you use these provided data in a publication or a scientific paper, please cite the dataset accordingly.
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Annotated image dataset of household objects from the RoboFEI@Home team
This data set contains two sets of pictures of household objects, created by the RoboFEI@Home team to develop object detection systems for a domestic robot.
The first data set was created with objects from a local supermarket. Product brands are typical from Brazil. The second data set is composed of objects from the RoboCup@Home 2018 OPL competition.
This data set contains two separate sets of annotated images. Common features of the image sets:
- Images are saved in JPG format
- Annotations are made with labelImg
- Both sets contain videos in MP4 format to test trained detection models
Set 1
166 annotated images with 1028 objects of the following 13 classes:
- cereal
- chocolate_milk
- heineken
- iron_man
- medicine
- milk_bottle
- milk_box
- monster
- purple_juice
- red_juice
- shampoo
- tea_box
- yellow_juice
There are also 28 videos for testing, shot with multiple smartphones.
Set 2
388 annotated images with 1737 objects of the following 20 classes:
- apple
- basket
- cereal
- chocolate_drink
- cloth_opl
- coke
- crackers
- grape_juice
- help_me_carry_opl
- noodles
- orange
- orange_juice
- paprika
- potato_chips
- pringles
- sausages
- scrubby
- sponge_opl
- sprite
- tray
There is also a single long video and 398 unannotated images for testing.
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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.
About 20 minutes of recorded material from the clear weather scenario, 13 minutes from the fog scenario, and about 15 minutes from rainy weather were processed. The longer videos were cut into sequences and from these sequences individual frames were extracted, resulting in 11,900 images for the clear weather, 4,905 images for the fog, and 7,030 images for the rainy weather scenarios.
A total of 6,111 frames were manual annotated so that could be used to train the supervised model for person detection. When selecting the frames, it was taken into account that the selected frames include different weather conditions so that in the set there were 2,663 frames shot in clear weather conditions, 1,135 frames of fog, and 2,313 frames of rain.
The annotations were made using the open-source Yolo BBox Annotation Tool that can simultaneously store annotations in the three most popular machine learning annotation formats YOLO, VOC, and MS COCO so all three annotation formats are available. The image annotation consists of a centroid position of the bounding box around each object of interest, size of the bounding box in terms of width and height, and corresponding class label (Human or Dog).
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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.
Download and extract the ZIP file containing all files. There is python code available (under 'scripts') to easily load the data set. Other programming languages should also handle .jpg, .hdf and .csv files for easy access. For easy access with python, a pickle dump file has been added. This has no extra information compared to the .csv file.
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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.
To facilitate use of some deep learning softwares, a folder tree with relative symbolic link (thus avoiding extra space) will gather all the sequences in three folders : | |— image | | — sequenceName0_imageNumber_timestamp0.jpg | | — sequenceName0_imageNumber_timestamp1.jpg | | — sequenceName0_imageNumber_timestamp2.jpg | | — sequenceName0_imageNumber_timestamp3.jpg | | — … | |— depth_8bits | | — sequenceName0_imageNumber_timestamp0.png | | — sequenceName0_imageNumber_timestamp1.png | | — sequenceName0_imageNumber_timestamp2.png | | — sequenceName0_imageNumber_timestamp3.png | | — … | |— annotations | | — sequenceName0_imageNumber_timestamp0.xml | | — sequenceName0_imageNumber_timestamp1.xml | | — sequenceName0_imageNumber_timestamp2.xml | | — sequenceName0_imageNumber_timestamp3.xml | | — … |
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