Asian Student Classroom Emotions Dataset
Bounding Box, Classification
Use Case: Asian student classroom Emotions Dataset
Format: Image
Count: 1k
Annotation: Yes
Description: The "Asian Student Classroom Emotions Dataset" is specifically designed for educational applications, featuring internet-collected images of Asian students in classroom settings, all at a uniform resolution of 1280 x 720 pixels. This dataset employs bounding box annotations and classification techniques to identify and categorize students' emotional and performance states in the classroom, aiming to enhance educational methodologies and student engagement strategies.
Asian Style Gestures Dataset
Bounding box, Tags
Use Case: Asian style Gestures Dataset
Format: Image
Count: 21,000
Annotation: Yes
Description: The "Asian Style Gestures Dataset" is curated for the visual entertainment industry, featuring a collection of internet-collected images with resolutions ranging from 530 x 360 to 2973 x 3968 pixels. This dataset specializes in annotations of hands displaying Asian style gestures, such as nods, hearts, rock, OK, putting hands together, clasping hands, etc., utilizing bounding boxes and tags for precise identification.
Hand Key Point Skeleton Dataset
Key Points
Use Case: Hand Key Point Skeleton Dataset
Format: Image
Count: 10k
Annotation: Yes
Description: The "Hand Key Point Skeleton Dataset" is designed for applications in visual entertainment and augmented/virtual reality (AR/VR), featuring a collection of indoor-collected images with a high resolution of 3024 x 4032 pixels. This dataset focuses on labeling 21 key points of the hand skeleton, capturing specific single-handed or two-handed poses such as forming a heart shape, placing a hand on the cheek, stretching, and more.
Human Posture Classification Dataset
Bounding Box, Tags
Use Case: Human Posture Classification Dataset
Format: Image
Count: 17k
Annotation: Yes
Description: The "Human Posture Classification Dataset" is designed for applications in visual entertainment and robotics, consisting of a collection of indoor-collected images with high resolutions exceeding 3024 x 4032 pixels. This dataset emphasizes bounding box annotations and tagging to identify half-body portraits and classify them into 14 distinct types of poses, such as crossed hands, hands around the head, and one hand on the cheek, among others.
Person Home Activity Dataset
Use Case: Motion Detection, Security Surveillance
Format: mp4
Count: 10002
Annotation: No
Description: Type 1: videos of people immediately outside of homes at front doors - Person walks toward/past the front door/home - Person walks away from the door/home - One or more person doing an extended activity (standing, looking around, talking) 6-20ft from doorbell. Type 2: videos of people inside the home engaging in certain actions - Sitting and eating, Working at desk, Reading, Sleeping, waking up and getting out of bed, Exercising / Dancing, Falling down, lying hurt on the floor
Recording Condition: Low Light: 20% - Ambient Indoor/Outdoor Lighting - Twilight/Golden Hour Regular Light: 40% - Normal Indoor/Outdoor - Uniformly Lit - Not Overly Saturated/Harsh Bright Light: 40% - Outdoor, Mid-Afternoon, Clear Sky - Indoor Natural Light, Or Brightly Lit - Avoid Over-Saturation Or Blown-Out Scenes
Video Highlight Moment Dataset
Classification (+ Time Tags)
Use Case: Video Highlight Moment Dataset
Format: Video
Count: 9k
Annotation: Yes
Description: Internet collected video clips with average length around 10s, and resolution is over 720 x 1280.
