A dataset of labelled objects on raw video sequences | Zendy

Hyomin Choi | Zendy; Elahe Hosseini | Zendy; Saeed Ranjbar Alvar | Zendy; Robert Cohen | Zendy; Ivan V. Bajić | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A dataset of labelled objects on raw video sequences

Author(s) -

Hyomin Choi,

Elahe Hosseini,

Saeed Ranjbar Alvar,

Robert Cohen,

Ivan V. Bajić

Publication year - 2020

Publication title -

data in brief

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.122

H-Index - 30

ISSN - 2352-3409

DOI - 10.1016/j.dib.2020.106701

Subject(s) - computer science , coding (social sciences) , artificial intelligence , object (grammar) , test set , pattern recognition (psychology) , set (abstract data type) , raw data , context (archaeology) , computer vision , mathematics , biology , statistics , programming language , paleontology

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research