# US 2019/0244366 A1 · Relevant Motion Detection in Video

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- Number: US 2019/0244366 A1
- Document ID: US20190244366A1
- Status: Published application
- Kind: A1
- Application number: 16/125,203
- Filed: 2018-09-07
- Published: 2019-08-08
- Earliest claimed: 2017-09-07
- Assignees: Comcast Cable Communications, LLC
- Applicants: Comcast Cable Communications, LLC
- Inventors: Ruichi Yu; Hongcheng Wang
- Web page: https://patentgrep.com/patent/US20190244366A1

## CPC

- G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/0495: Quantised networks; Sparse networks; Compressed networks
- G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning
- G06N 3/09: Supervised learning
- G06N 5/046: Forward inferencing; Production systems
- G06T 7/254: involving subtraction of images
- G06N 3/08: Learning methods
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30232: Surveillance
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Methods, systems, and/or apparatuses are described for detecting relevant motion of objects of interest (e.g., persons and vehicles) in surveillance videos. As described herein input data based on a plurality of captured images and/or video is received. The input data may then be pre-processed and used as an input into a convolution network that may, in some instances, have elements that perform both spatial-wise max pooling and temporal-wise max pooling. Based on The convolution network may be used to generate a plurality of prediction results of relevant motion of the objects of interest.

## Claims

### Claim 1

A method comprising:
generating, by a computing device and based on image data, data based on a plurality of images;
determining, from the generated data and using one or more first convolutional layers, feature data indicative of one or more of: an appearance-related feature or a motion-related feature;
determining, based on the feature data and using one or more second convolutional layers, predicted motion of one or more objects in the plurality of images; and
determining, based on the predicted motion, that an object of the one or more objects has moved.