TIDCB: Text Image Dangerous-Scene Convolutional Baseline
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Abstract
The automatic management of public area safety is one of the most challenging issues in our society. For instance, the timely evacuation of the public during incidents such as fires or large-scale shootings is paramount. However, detecting pedestrian behavior indicative of danger promptly from extensive video surveillance data may not always be feasible. This may result in untimely warnings being provided, resulting in signicant loss of life. Although existing research has proposed text-based person search, it has primarily focused on pedestrian search by matching images of pedestrian body parts to text, lacking the search for pedestrians in dangerous scenarios. To address this gap, this paper proposes an innovative warning framework that further searches for individuals in hazardous situations based on textual descriptions, aiming to prevent or mitigate crisis events. We have constructed a new public safety dataset named CHUK-PEDES-DANGER, one of the first pedestrian datasets that includes dangerous scenes. Additionally, we introduce a novel framework for public automatic evacuation. This framework leverages a multimodal deep learning architecture that combines the image model ResNet-50 with the text model RoBERTa to produce our Text-Image Dangerous-Scene Convolutional Baseline (TIDCB) model, which addresses the classification problem from text to image and image to text by matching images of pedestrian body parts and environments to text. We propose a novel loss function, cross-modal projection matching-triplet (CMPM-Triplet). After conducting extensive experiments, we have validated that our method significantly improves accuracy. Our model outperforms TIPCB with a matching rate of 76.93%, an improvement of 4.78% compared to TIPCB, and demonstrates significant advantages in handling complex scenarios.
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