Semi-Supervised Object Labeling on Video Data with Collaborative Classification and Active Learning

Bruno Padilha, João Eduardo Ferreira

2025

Abstract

Streaming applications in video monitoring networks generate datasets that are continuously expanding in terms of data amount and sources. Thus, given the sheer amount of data in these scenarios, one big and fundamental challenge is how to reliably automate data annotation for downstream tasks such as object detection, image classification, object tracking among other functionalities. In this work, we propose a novel active learning strategy based on multi-model collaboration able to self-annotate training data, providing only a small initial subset of human verified labels, towards incremental model improvement and distribution shifts adaptation. To validate our approach, we collected approximately 50,000 hours of video data sourced from 193 security cameras from University of São Paulo Monitoring System (USP-EMS) during the years 2021-2023, totaling 7.3TB of raw data. For experimental purposes, this work is focused on identification of pedestrians, cyclists and motorcyclists resulting in 3.5M unique objects labeled with accuracy between 92% to 96% for all cameras. Time-stamped data along with our incremental learning method also facilitate management of naturally occurring distribution shifts (e.g., weather conditions, time of the year, dirty lenses, out-of-focus cameras). We are currently working to release this dataset in compliance with local data privacy legislation.

Paper Citation

in Harvard Style

Padilha B. and Ferreira J. (2025). Semi-Supervised Object Labeling on Video Data with Collaborative Classification and Active Learning. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KMIS; ISBN 978-989-758-769-6, SciTePress, pages 247-256. DOI: 10.5220/0013707800004000

in Bibtex Style

@conference{kmis25,
author={Bruno Padilha and João Ferreira},
title={Semi-Supervised Object Labeling on Video Data with Collaborative Classification and Active Learning},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KMIS},
year={2025},
pages={247-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013707800004000},
isbn={978-989-758-769-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KMIS
TI - Semi-Supervised Object Labeling on Video Data with Collaborative Classification and Active Learning
SN - 978-989-758-769-6
AU - Padilha B.
AU - Ferreira J.
PY - 2025
SP - 247
EP - 256
DO - 10.5220/0013707800004000
PB - SciTePress