A Dataset of Labelled Objects on Raw Video Sequences

Peer reviewed: 
Yes, item is peer reviewed.
Scholarly level: 
Faculty/Staff
Final version published as: 

Choi, H., Hosseini, E., Ranjbar Alvar, S., Cohen, R. A., & Bajić, I. V. (2021). A dataset of labelled objects on raw video sequences. Data in Brief, 34, 106701. https://doi.org/10.1016/j.dib.2020.106701.

Date created: 
2020-12-26
Identifier: 
DOI: 10.1016/j.dib.2020.106701
Keywords: 
Object detection
Video coding
Video compression
Video coding for machines
Abstract: 

We present an object labelled dataset called SFU-HW-Objects-v1, which contains object labels for a set of raw video sequences. The dataset can be useful for the cases where both object detection accuracy and video coding efficiency need to be evaluated on the same dataset. Object ground-truths for 18 of the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences have been labelled. The object categories used for the labeling are based on the Common Objects in Context (COCO) labels. A total of 21 object classes are found in test sequences, out of the 80 original COCO label classes. Brief descriptions of the labeling process and the structure of the dataset are presented.

Language: 
English
Document type: 
Article
File(s): 
Sponsor(s): 
Huawei
Natural Sciences and Engineering Research Council of Canada (NSERC)
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