Anomaly detection is critically important for intelligent surveillance systems to detect in a timely manner any malicious activities. Many video anomaly detection approaches using deep learning methods focus on a single camera video stream with a fixed scenario. These deep learning methods use large-scale training data with large complexity. As a solution, in this paper, we show how to use pre-trained convolutional neural net models to perform feature extraction and context mining, and then use denoising autoencoder with relatively low model complexity to provide efficient and accurate surveillance anomaly detection, which can be useful for the resource-constrained devices such as edge devices of the Internet of Things (IoT). Our anomaly detection model makes decisions based on the high-level features derived from the selected embedded computer vision models such as object classification and object detection. Additionally, we derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method. We use two UCSD datasets to demonstrate that our approach with relatively low model complexity can achieve comparable performance compared to the state-of-the-art approaches.

No abstract

No abstract

No abstract

The objective of this study is to establish a comprehensive evaluation system for military hospitals' response capacity to bio-terrorism. Literature research and Delphi method were utilized to establish the comprehensive evaluation system for military hospitals' response capacity to bio-terrorism. Questionnaires were designed and used to survey the status quo of 134 military hospitals' response capability to bio-terrorism. Survey indicated that factor analysis method was suitable to for analyzing the comprehensive evaluation system for military hospitals' response capacity to bio-terrorism. The constructed evaluation system was consisted of five first-class and 16 second-class indexes. Among them, medical response factor was considered as the most important factor with weight coefficient of 0.660, followed in turn by the emergency management factor with weight coefficient of 0.109, emergency management consciousness factor with weight coefficient of 0.093, hardware support factor with weight coefficient of 0.078, and improvement factor with weight coefficient of 0.059. The constructed comprehensive assessment model and system are scientific and practical.