We empirically test the capacity of an improved system to identify not just images of individual guns, but partially occluded guns and their parts appearing in a videoframe. This approach combines low-level geometrical information gleaned from the visual images and high-level semantic information stored in an ontology enriched with meronymic part-whole relations. The main improvements of the system are handling occlusion, new algorithms, and an emerging meronomy. Well-known and commonly deployed in ontologies, actual meronomies need to be engineered and populated with unique solutions. Here, this includes adjacency of weapon parts and essentiality of parts to the threat of and the diagnosticity for a weapon. In this study video sequences are processed frame by frame. The extraction method separates colors and removes the background. Then image subtraction of the next frame determines moving targets, before morphological closing is applied to the current frame in order to clean up noise and fill gaps. Next, the method calculates for each object the boundary coordinates and uses them to create a finite numerical sequence as a descriptor. Parts identification is done by cyclic sequence alignment and matching against the nodes of the weapons ontology. From the identified parts, the most-likely weapon will be determined by using the weapon ontology.

Date Published: 12 May 2016
PDF: 10 pages
Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440Y (12 May 2016); doi: 10.1117/12.2224344

Published in SPIE Proceedings Vol. 9844:
Automatic Target Recognition XXVI
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)