Current surveillance and control systems still require human supervision and intervention. This work presents a novel automatic handgun detection system in videos appropriate for both, surveillance and control purposes. We reformulate this detection problem into the problem of minimizing false positives and solve it by building the key training data-set guided by the results of a deep Convolutional Neural Networks (CNN) classifier, then assessing the best classification model under two approaches, the sliding window approach and region proposal approach. The most promising results are obtained by Faster R-CNN based model trained on our new database. The best detector show a high potential even in low quality youtube videos and provides satisfactory results as automatic alarm system. Among 30 scenes, it successfully activates the alarm after five successive true positives in less than 0.2 seconds, in 27 scenes. We also define a new metric, Alarm Activation per Interval (AApI), to assess the performance of a detection model as an automatic detection system in videos.
Transfer Learning-based Real-time Handgun Detection
Laboratoire LITAN, École supérieure en Sciences et Technologies de l’Informatique et du Numérique（LITAN实验室，计算机与数字科学技术高等学院）；SGRE-lab, University Tahri Mohammed of Bechar（SGRE实验室，贝沙尔塔赫里·穆罕默德大学）
Automatic Seizure Detection Using the Pulse Transit Time
Fraunhofer Institute for Software and Systems Engineering（弗劳恩霍夫软件与系统工程研究所）；University of Bonn Medical Center（波恩大学医学中心）；TU Dortmund University（多特蒙德工业大学）
SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience
IBM Research Australia（IBM澳大利亚研究院）
跌倒可能会对老年人造成致命的后果，特别是如果跌倒的人由于失去知觉或受伤而无法呼救。自动跌倒检测系统可以通过及时的跌倒警报和最大限度地减少在家中独立生活时对跌倒的恐惧来提供帮助。现有的基于视觉的跌倒检测系统由于诸如物理外观的变化、不同的相机视点、遮挡和背景杂波的挑战而缺乏对看不见的环境的概括。在本文中，我们将探索克服上述挑战的方法，并提出Single Shot Human Fall Detector（SSHFD），这是一个基于深度学习的框架，用于从单个图像中自动检测跌倒。这是通过两项关键创新实现的。首先，我们提出了一个基于姿态的跌倒表示，这是不变的外观特征。其次，我们提出了神经网络模型的三维姿态估计和下降识别，这是弹性的缺失关节，由于闭塞的身体部位。在公共跌倒数据集上的实验表明，我们的框架成功地将纯粹从合成数据中学习的3d姿势估计和跌倒识别知识转移到看不见的真实世界数据中，展示了其在真实世界场景中准确跌倒检测的泛化能力。
An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening
Department of Computer Science, Durham University, United Kingdom（英国杜伦大学计算机科学系）；Department of Engineering, Durham University, United Kingdom（英国杜伦大学工程系）
