  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.
