US 12,254,659 B2
Hybrid video analytics for small and specialized object detection
Peter L. Venetianer, McLean, VA (US); Burak Kakillioglu, Syracuse, NY (US); Aleksey Lipchin, Newton, MA (US); and Xiao Xiao, Winchester, MA (US)
Assigned to MOTOROLA SOLUTIONS, INC., Chicago, IL (US)
Filed by MOTOROLA SOLUTIONS, INC., Chicago, IL (US)
Filed on May 4, 2022, as Appl. No. 17/662,050.
Prior Publication US 2023/0360355 A1, Nov. 9, 2023
Int. Cl. G06V 10/20 (2022.01); G06T 7/194 (2017.01); G06V 10/82 (2022.01); G06V 20/52 (2022.01)
CPC G06V 10/255 (2022.01) [G06T 7/194 (2017.01); G06V 10/82 (2022.01); G06V 20/52 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A video surveillance system comprising:
a video camera configured to capture a video; and
an object detector in communication with the video camera and including an electronic processor configured to
receive the video from the video camera,
detect a plurality of candidate objects in the video using a convolutional neural network detection process and a background subtraction detection process;
identify a candidate object from the plurality of candidate objects, the candidate object detected by the background subtraction detection process in a location of the video with no candidate objects detected by the convolutional neural network detection process;
determine a background subtraction confidence level of the candidate object;
categorize the candidate object as a detected object in the video in response to the background subtraction confidence level satisfying a background subtraction confidence threshold; and
in response to the background subtraction confidence level not satisfying the background subtraction confidence threshold
perform an additional convolutional neural network detection process on a location of the candidate object;
determine an additional convolutional neural network confidence level of the candidate object; and
categorize the candidate object as the detected object in the video in response to the additional convolutional neural network confidence level satisfying an additional convolutional neural network confidence threshold.