AImageLab datasets

M-VAD Names

The dataset consists of 84.6 hours of video from 92 Hollywood movies. For each movie, we provide the annotations of the characters' visual appearances, in the form of tracks of face bounding boxes, and the associations with the characters' textual mentions, when available. The released dataset contains more than 24k annotated video clips, including 63k visual tracks and 34k textual mentions, all associated with their character identities. 

AiC Dataset

AiC (Attributes in Crowd) is a novel synthetic dataset for people attribute recognition in presence of strong occlusions created by exploiting the highly photorealistic video game Grand Theft Auto V developed by Rockstar North. It features 125,000 samples, all being a unique person, each of which is automatically labeled with information concerning visual attributes, as well as joint locations.

Artpedia

Artpedia contains a collection of 2,930 painting images, each associated to a variable number of textual descriptions. Each sentence is labelled either as a visual sentence or as a contextual sentence, if does not describe the visual content of the artwork. Overall, the dataset contains a total of 28,212 sentences, 9,173 labelled as visual sentences and the remaining 19,039 as contextual sentences. On average, each painting is associated with 3.1 visual and 6.5 contextual sentences.

Watch-R-Patch

Watch-R(efined)-Patch it's a more precise and accurate version of the public dataset Watch-N-Patch, which is composed by different actions from diffrent people acquired with Kinect One. The annotations in the original datasets are computed with the Kinect SDK, which is not reliable for generic actions in non-frontal position. We collect refined annotations using a quick and easy-to-use annotation tool, publicly released here. Hand-corrected joints gave us the opportunity to compare our result with real information.

Briareo dataset

We propose a new dataset, called Briareo, specifically collected for the hand gesture recognition task in the automotive context. We focus on the acquisition of dynamic hand gestures, where each gesture is a combination of motion and one or more hand poses. Images have been collected from an innovative point of view: the acquisition devices are placed in the central tunnel between the driver and the passenger seats, orientated towards the car ceiling. In this way, visual occlusions produced by driver’s body can be mitigated.

JTA Dataset

JTA is a synthetic large-scale dataset for human pose estimation and tracking in video acquired using the video game Grand Theft Auto 5 developed by Rockstar North. The recorded videos are chosen to contain a large amount of body pose appearance and scale variation, as well as body part occlusion and truncation. The videos also contain severe body motion, i.e., people occlude each other, re-appear after complete occlusion, vary in scale across the video, and also significantly change their body pose. The number of visible persons and body parts may also vary during the video. Furthermore, it provides a diversity of viewpoints including a rich variety of angles and camera movements.

Turms

We aim to propose a new dataset, namely Turms, that contains infrared images of hands acquired placing an infrared camera -- a Leap Motion device -- that is able to provide high-quality and short-range images with a wide scene, thanks to its fish-eye lens. 
The presence of a stereo camera allows the possibility of recovering depth and 3D information. 
We collect images from an innovative point of view -- back the steering wheel, very close to the hands --. 

Pandora

Pandora has been specifically created for head center localization, head pose and shoulder pose estimation and is inspired by the automotive context. A frontal fixed device acquires the upper body part of the subjects, simulating the point of view of camera placed inside the dashboard. Subjects also perform driving-like actions, such as grasping the steering wheel, looking to the rear-view or lateral mirrors, shifting gears and so on.
Pandora contains more than 250k full resolution RGB (1920x1080 pixels) and depth images (512x424) with the corresponding annotation; 110 annotated sequences using 10 male and 12 female actors.
Garments as well as various objects are worn or used by the subjects to create head and/or shoulder occlusions. For example, people wear prescription glasses, sun glasses, scarves, caps, and manipulate smartphones, tablets or plastic bottles.

MotorMark

MotorMark is composed by more than 30k frames. A variety of subjects is guarantee (35 subjects in total).
We recreate an automotive context. The subject is standing in a real car dashboard and performs real inside-car actions, like rotating steering wheel, shifting gears.
Subjects are asked to follow a constrain path (4 led are placed in correspondence with the speedometer, the rev counter, infotainment system and the left wing mirror), to rotate their head in fixed position or to freely move their head. Besides, subjects can wear glasses, sun glasses and a scarf, to generate partial face and landmark occlusions.
The annotation of 68 landmark positions on both RGB and depth frames is available, following the ISO MPEG-4 standard. The ground truth has been manually generated.
The user was provided with an initial estimation done by means of the algorithm included in the dLib libraries (www.dlib.net), which gives landmark positions on RGB images. The projection of the landmark coordinates on the depth images is carried out exploiting the internal calibration tool of the Microsoft Kinect SDK.  
RGB and depth images are acquired with a spatial resolution of 1280x720 HD and 515x424, respectively;

DR(eye)VE

DR(eye)VE: a dataset for attention-based tasks with applications to autonomous and assisted driving.
Composed by 74 video sequences of 5 mins each, we have captured and annotated more than 500,000 frames.
The labeling contains drivers’ gaze fixations and their temporal integration providing task-specific saliency maps.
Geo-referenced locations, driving speed and course complete the set of released data.

BBC Planet Earth Dataset

The BBC Planet Earth dataset contains ground truth shots and scene annotation for each of the 11 eposides of the BBC Planet Earth educational TV Series. Each shot and scene has been manually annotated and verified by a set of human experts. Moreover, being scene detection a subjective task, we collect scene annotations from 5 different people.

Surrounding Vehicles Awareness dataset

In order to collect data, we exploit Script Hook V library synthetic_scripthook, which allows using Grand Theft Auto V (GTAV) video game native functions.
We develop a framework in which the game camera automatically toggle between frontal and bird-eye view at each game time step: in this way we are able to gather information about the spatial occupancy of the vehicles in the scene from both views (bounding boxes, distances, yaw rotations).
We associate vehicles information across the two views by querying the game engine for entity IDs. 

Xdocs dataset

Xdocs is designed with the intention of extending to a much wider audience the possibility to access a variety of historical documents. To that purpose, a great amount of Italian historical birth certificates documents of the XIX century has been collected. Moreover, to test and evaluate the Xdocs tools (Word Spotting and Annotation), a huge collection of single word images has been collected and annotated.

• Personal Information Data
  This is a newly collected dataset consisting of annotated words images of names, surnames, birthdays, municipalities and sex. All images are taken from Italian civil registries of the XIX century. Writing styles variety is guaranteed due to the presence of different writers.
   
• Month Data
  This sub-dataset consists of a collection of handwritten month names extracted from Italian civil registries of the XIX century.
  Specifically, the dataset counts 1200 words of all 12 months: data are affected by both intra and inter variations, due to the presence of three different official state writers.  

Kinteract

We collect a new dataset which has been explicitly designed and created for Human Computer Interaction.
10 gestures types: zoom in, zoom out, scroll up, scroll down, slide left, slide right, rotate, back, ok, exit; dataset contains also a no action, in which subjects stands in front of the camera with a neutral pose.
Gestures are performed by 10 subjects (not all subjects perform all actions), for a total of 168 instances. They are acquired by standing in front of a stationary Kinect 1 device and only the upper part of the body  (shoulders, elbows, wrists and hands) is involved; each gesture is performed with the same arm by all the subjects (despite they are left or right handed). 

YACCLAB

Following a common practice in the literature, we built a dataset that includes both synthetic and real images. The provided dataset is suitable for a wide range of applications, ranging from document processing to survaillance, and features a significant variability in terms of resolution, image density and number of components. All images are provided in 1 bit per pixel PNG format, with 0 (black) being background and 1 (white) being foreground.

Maramotti Dataset for Gesture Recogntion

This dataset contains videos taken in the Maramotti modern art museum, in which paintings, sculptures and objets d’art are exposed. The camera is placed on the user’s head and captures a 800x450, 25 frames per second image sequence. The Maramotti dataset contains 700 video sequences, recorded by five different persons, each performing seven hand gestures in front of different artworks: like, dislike, point, ok, slide left to right, slide right to left and take a picture. Some of them (like the point, ok, like and dislike gestures) are statical, others (like the two slide gestures) are dynamical.

Interactive Museum for Gesture Recognition

The Interactive Museum dataset consists of 700 video sequences, all shot with a wearable camera, taken in a interactive exhibition room, in which paintings and artworks are projected over a wall in a virtual museum fashion. The camera is placed on the user’s head and captures a 800x450, 25 frames per second 24-bit RGB image sequence. Five different users perform seven hand gestures: like, dislike, point, ok, slide left to right, slide right to left and take a picture.

3dPeS

3DPeS (3D People Surveillance Dataset) is a surveillance dataset, designed mainly for people re-identification in multi camera systems with non-overlapped field of views, but also applicable to many other tasks, such as people detection, tracking, action analysis and trajectory analysis.

Available data: the camera setting and the 3D environment reconstruction, the hundreds of recorded videos, the camera calibration parameters, the identity of the hundreds of people, detected more than one time by different point of view.

It contains numerous video sequences taken from a real surveillance setup, composed by 8 different surveillance cameras, monitoring a section of the campus of the University of Modena and Reggio Emilia. Data were collected over the course of several days.
 

Visor - VIdeo Surveillance Online Repository

ViSOR contains a large set of multimedia data and the corresponding annotations. The repository has been conceived as a support tool for different research projects.
Together with the videos, ViSOR contains metadata annotation, both manually annotated ground-truth data and automatically obtained outputs of a particular system. In such a manner, the users of the repository are able to perform validation tasks of their own algorithms as well as comparative activities. ViSOR also contains two datasets for people Reidentification: 3DPES and SARC3D.