Intelligent resident surveillance is one of the most essential smart community services. The increasing demand for security needs surveillance systems to be able to detect anomalies in surveillance scenes. Employing high-capacity computational devices for intelligent surveillance in residential societies is costly and not feasible. Therefore, we propose anomaly detection for intelligent surveillance using CPU-only edge devices. A modular framework to capture object-level inferences and tracking is developed. To cope with partial occlusions, posture deformations, and complex scenes we employed feature encoding and trajectory associations. Elements of the anomaly detection framework are optimized to run on CPU-only edge devices with sufficient FPS. The experimental results indicate the proposed method is feasible and achieves satisfactory results in real-life scenarios.
翻译:智能居民监视是最基本的智能社区服务之一。对安全监视系统的需求日益增加,需要能够发现监视场景中的异常现象。在居民社会使用高容量的智能监视计算装置是昂贵的,也是不可行的。因此,我们提议使用CPU专用边缘装置对智能监视进行异常检测。开发了一个模块化框架,以捕捉物体级别的推理和跟踪。为了应对部分隔离、态势变形和复杂的场景,我们使用了特征编码和轨迹关联。异常检测框架的要点最优化,可以运行在只有CPU专用的边缘装置上,有足够的FPS。实验结果表明,拟议的方法是可行的,在现实生活中可以取得令人满意的结果。