Despite rapid advances in image-based machine learning, the threat identification of a knife wielding attacker has not garnered substantial academic attention. This relative research gap appears less understandable given the high knife assault rate (>100,000 annually) and the increasing availability of public video surveillance to analyze and forensically document. We present three complementary methods for scoring automated threat identification using multiple knife image datasets, each with the goal of narrowing down possible assault intentions while minimizing misidentifying false positives and risky false negatives. To alert an observer to the knife-wielding threat, we test and deploy classification built around MobileNet in a sparse and pruned neural network with a small memory requirement (< 2.2 megabytes) and 95% test accuracy. We secondly train a detection algorithm (MaskRCNN) to segment the hand from the knife in a single image and assign probable certainty to their relative location. This segmentation accomplishes both localization with bounding boxes but also relative positions to infer overhand threats. A final model built on the PoseNet architecture assigns anatomical waypoints or skeletal features to narrow the threat characteristics and reduce misunderstood intentions. We further identify and supplement existing data gaps that might blind a deployed knife threat detector such as collecting innocuous hand and fist images as important negative training sets. When automated on commodity hardware and software solutions one original research contribution is this systematic survey of timely and readily available image-based alerts to task and prioritize crime prevention countermeasures prior to a tragic outcome.

中文翻译：

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刀具和威胁探测器

尽管基于图像的机器学习取得了快速进展，但对持刀攻击者的威胁识别并没有引起学术界的广泛关注。鉴于高刀袭击率（每年超过 100,000 次）以及用于分析和法医记录的公共视频监控的可用性越来越高，这种相对的研究差距似乎不太容易理解。我们提出了三种使用多刀图像数据集对自动威胁识别进行评分的互补方法，每种方法的目标都是缩小可能的攻击意图，同时最大限度地减少误判误报和危险的漏报。为了提醒观察者注意持刀威胁，我们在具有少量内存要求（< 2.2 兆字节）和 95% 测试准确率的稀疏和修剪神经网络中测试和部署围绕 MobileNet 构建的分类。其次，我们训练检测算法 (MaskRCNN) 在单个图像中将手与刀分开，并为它们的相对位置分配可能的确定性。这种分割既完成了边界框的定位，也完成了推断上手威胁的相对位置。建立在 PoseNet 架构上的最终模型分配解剖路径点或骨架特征，以缩小威胁特征并减少误解的意图。我们进一步识别和补充可能使部署的刀具威胁检测器失明的现有数据差距，例如收集无害的手和拳头图像作为重要的负面训练集。