US 11,907,293 B2
Reasoning from surveillance video via computer vision-based multi-object tracking and spatiotemporal proximity graphs
Zachary Jorgensen, Aurora, CO (US); Tyler Staudinger, Denver, CO (US); and Charles Viss, Denver, CO (US)
Assigned to CACI, Inc.—Federal, Reston, VA (US)
Filed by CACI, Inc.—Federal, Arlington, VA (US)
Filed on Dec. 14, 2020, as Appl. No. 17/120,349.
Prior Publication US 2022/0188356 A1, Jun. 16, 2022
Int. Cl. G06F 16/787 (2019.01); G06F 16/901 (2019.01); G06F 40/56 (2020.01); G06N 3/08 (2023.01); G06F 16/75 (2019.01); G06V 20/40 (2022.01)
CPC G06F 16/787 (2019.01) [G06F 16/75 (2019.01); G06F 16/9024 (2019.01); G06F 40/56 (2020.01); G06N 3/08 (2013.01); G06V 20/47 (2022.01)] 12 Claims
OG exemplary drawing
 
1. A method comprising:
receiving a scene including a plurality of tracks, each track of the plurality of tracks corresponding to an entity and each track of the plurality of tracks being associated with a bounding box;
determining, based on a distance between a plurality of the bounding boxes, a defined spatial proximity between the plurality of the bounding boxes;
building a spatiotemporal proximity graph based on the plurality of tracks; and
identifying, based on the spatiotemporal proximity graph and the spatial proximity, a spatiotemporal relationship between a plurality of the entities.