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Related Experiment Video

Updated: Nov 21, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

A dataset of labelled objects on raw video sequences.

Hyomin Choi¹, Elahe Hosseini¹, Saeed Ranjbar Alvar¹

¹School of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.

|January 18, 2021

Summary

A new dataset, SFU-HW-Objects-v1, provides object labels for video sequences, aiding evaluation of object detection and High Efficiency Video Coding (HEVC) efficiency.

Keywords:

Object detection Video coding Video coding for machines Video compression

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Area of Science:

Computer Vision
Video Compression
Machine Learning

Background:

Evaluating object detection and video coding efficiency often requires separate datasets.
A unified dataset facilitates simultaneous assessment of both metrics.

Purpose of the Study:

To introduce the SFU-HW-Objects-v1 dataset for evaluating object detection and video coding efficiency.
To provide object ground-truth labels for High Efficiency Video Coding (HEVC) Common Test Conditions (CTC) sequences.

Main Methods:

Object ground-truths were manually labeled for 18 HEVC v1 CTC sequences.
Labels were derived from the Common Objects in Context (COCO) dataset categories.
A subset of 21 COCO object classes were identified and labeled within the test sequences.

Main Results:

The SFU-HW-Objects-v1 dataset contains object labels for raw video sequences.
It includes ground-truth data for 21 object classes relevant to video coding evaluation.
The dataset structure and labeling process are documented.

Conclusions:

The SFU-HW-Objects-v1 dataset enables joint evaluation of object detection and video coding efficiency.
This resource supports research in optimizing video compression for content with detected objects.