Suspicious Loitering Action Recognition Dataset

The current security industry faces increasing security risks, especially in public places, making the monitoring of suspicious behavior a key challenge. Existing security systems often struggle to effectively and promptly identify suspicious individuals loitering in non-open or dangerous areas, leading to potential security risks not being addressed in time. This dataset aims to provide high-quality video of suspicious loitering behavior, supporting the training and evaluation of deep learning models to enhance the responsiveness of security monitoring systems. Data is collected from multiple surveillance cameras in real-world environments, undergoing strict quality control processes, including multiple rounds of annotation and expert review to ensure data accuracy and consistency. The data is stored in MP4 format, with each video containing corresponding action labels and timestamp information for subsequent analysis and training. The core advantage of this dataset is its high annotation precision and consistency, effectively supporting the training of various deep learning models and improving action recognition accuracy to over 90%. By adopting new annotation methods and quality assessment techniques, the dataset's application value is significantly enhanced, helping the security industry to rapidly respond to potential security threats.