Surveillance Video-and-Language Understanding: From Small to Large Multimodal Models

Abstract

Surveillance videos play a crucial role in public security. However, current tasks related to surveillance videos primarily focus on classifying and localizing anomalous events. Despite achieving notable performance, existing methods are restricted to detecting and classifying predefined events and lack satisfactory semantic understanding. To tackle this challenge, we introduce a novel research avenue focused on Video-and-Language Understanding for surveillance (VALU), and construct the first multimodal surveillance video dataset. We manually annotate the real-world surveillance dataset UCF-Crime with fine-grained event content and timing. Our newly annotated dataset, UCA (UCF-Crime Annotation), contains 23,542 sentences, with an average length of 20 words, and its annotated videos are as long as 110.7 hours. Moreover, we evaluate SOTA models on five multimodal tasks using this newly created dataset, establishing new baselines for surveillance VALU, from small to large models. Our experiments reveal that mainstream models, which perform well on previously public datasets, exhibit poor performance on surveillance video, highlighting new challenges in surveillance VALU. In addition to conducting baseline experiments to compare the performance of existing models, we also propose novel methods for multimodal anomaly detection tasks and finetune multimodal large language model models using our dataset. All the experiments highlight the necessity of constructing this multimodal dataset to advance surveillance AI. Upon the experimental results mentioned above, we conduct further in-depth analysis and discussion. The dataset and codes are provided at https://xuange923.github.io/Surveillance-Video-Understanding.