Collaborative Classification for Object Labeling on Expansible Datasets

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. 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 evaluated cameras.