Handball action dataset - UNIRI-HBD

Citation Author(s):
Marina
Ivasic-Kos
Department of Informatics University of Rijeka
Miran
Pobar
Department of Informatics University of Rijeka
Submitted by:
Miran Pobar
Last updated:
Thu, 04/29/2021 - 11:20
DOI:
10.21227/0g0a-fe06
Data Format:
*.mp4
*.csv (zip)
Links:
Building a labeled dataset for recognition of handball actions using mask R-CNN and STIPS
Active Player Detection in Handball Videos Using Optical Flow and STIPs Based Measures
Active Player Detection in Handball Scenes Based on Activity Measures
License:
Creative Commons Attribution
1084 Views
Categories:
Image Processing
Computer Vision
Machine Learning
Keywords:
Action recognition, Sports Analytics, object detection
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Abstract 

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 scenes were shot with stationary GoPro cameras that were mounted on the left or right side of the playground, from different angles. The videos were recorded in at least full HD (1920 × 1080) resolution at 30 or more frames per second. 

On average 12 players appear in each scene, but mostly one or two players perform the action of interest. The performances of the actions of interest are extracted in separate video clips showing only that action in isolation. The isolated action clips are obtained first by automatic detection of players using Mask RCNN detector and tracking with Deep SORT, then by manual selection of appropriate tracked player.

Instructions: 

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.

Comments

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Submitted by Aparnesh Murthy on Tue, 06/22/2021 - 11:34

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