Uso de Características Temporais e Semânticas para Detectar Eventos em Vídeos de Violência Urbana - Rocha, Saul Sousa da; Vale e Silva, Carlos Henrique; Silva, Mateus José da; Torres Neto, Jose Rodrigues; Ferreira, Carlos Henrique G.; Gonçalves, Glauber Dias (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia), 2025)
Videos published on platforms such as YouTube play a central role in covering urban violence events, but the high lexical similarity between different occurrences and the temporal proximity of postings hinder the automatic identification of which videos refer to the same event. Previous approaches predominantly explore semantic characteristics extracted from video, audio, and metadata, often relying on supervised techniques that require high computational costs and extensive annotations, making them impractical for continuous large-scale monitoring. Addressing this problem is of great importance for applications such as integrating multimedia records in investigations, fact-checking, assessing the impact of events, and building reliable historical archives. This work proposes two unsupervised and complementary heuristics: one based on temporal characteristics and anchor entities extracted via named entity recognition, and another based on multiple semantic attributes integrated into a similarity graph. We evaluate these approaches on a novel dataset of more than 1,400 manually annotated videos, collected between 2019 and 2024, covering both low- and high-impact events. Results show that the temporal heuristic consistently outperforms both the semantic heuristic and GPT-4 as a baseline, achieving accuracy up to 0.90 and NMI up to 0.98, while the semantic heuristic performs better in sparse-event scenarios. We also find that including full transcripts brings no substantial gains, indicating that titles and descriptions already contain the most relevant information for the task. These findings reinforce the potential of simple, low-cost, domain-adapted solutions to outperform generic approaches in challenging video clustering scenarios.