In an era of rapid technological advancements, computer systems play a crucial role in early Violence Detection (VD) and localization, which is critical for timely human intervention. However, existing VD methods often fall short, lacking applicability to surveillance data, and failing to address the localization and social dimension of violent events. To address these shortcomings, we propose a novel approach to integrate subgroups into VD. Our method recognizes and tracks multiple subgroups across frames, providing an additional layer of information in VD. This enables the system to not only detect violence at video-level, but also to identify the groups involved. This adaptable add-on module can enhance the applicability of existing models and algorithms. Through extensive experiments on the SCFD and RWF-2000 surveillance datasets, we find that our approach improves social awareness in real-time VD by localizing the people involved in an act of violence. The system offers a small performance boost on the SCFD dataset and maintains performance on RWF-2000, reaching 91.3% and 87.2% accuracy respectively, demonstrating its practical utility while performing close to state-of-the-art methods. Furthermore, our efficient method generalizes well to unseen datasets, marking a promising advance in early VD.

In this paper we propose an approach for automatically recognizing ancient Egyptian hieroglyph from photographs. To this end we first manually annotated and segmented a large collection of nearly 4,000 hieroglyphs. In our automatic approach we localize and segment each individual hieroglyph, determine the reading order and subsequently evaluate 5 visual descriptors in 3 different matching schemes to evaluate visual hieroglyph recognition. In addition to visual-only cues, we use a corpus of Egyptian texts to learn language models that help re-rank the visual output.