activity-recognition
mobilenet_v2-activity-recognitionHuman-Activity-Recognition-with-Smartphonesdeit-base-distilled-patch16-224cabin_activity_recognitionvit-tiny-patch16-224cabin_activity_recognitionHuman-Activity-RecognitionHuman_Activity_Recognition_Using_SmartphoneHuman_Activity_Recognition_using_Video_ClassificationHuman-Activity-Recognition-LSTM
Collective-Activity-Recognition
Annotation Format
Every 10th frame in all video sequences was manually annotated with the following information for each detected person:
Bounding box location
Activity class
Pose direction
Annotation Fields
Each annotation follows the format:
<frame_number> <x> <y> <width> <height> <class_id> <pose_id>
Field
Description
frame_number
Frame identifier
x
X-coordinate of the bounding box (top-left corner)
y
Y-coordinate of the bounding box (top-left… See the full description on the dataset page: https://huggingface.co/datasets/litforth/Collective-Activity-Recognition.Human_Activity_RecognitionHuman Activity Recognition (HAR) using smartphones dataset. Classifying the type of movement amongst five categories:
WALKING,
WALKING_UPSTAIRS,
WALKING_DOWNSTAIRS,
SITTING,
STANDING
The experiments have been carried out with a group of 16 volunteers within an age bracket of 19-26 years. Each person performed five activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING) wearing a smartphone (Samsung Galaxy S8) in the pucket. Using its embedded accelerometer and gyroscope… See the full description on the dataset page: https://huggingface.co/datasets/DiFronzo/Human_Activity_Recognition.human-activity-recognitionConstruction_Activity_Recognition_datasetActivity_Human_RecognitionConstruction_Activity_Recognition_dataset