Image processing and object detecting system, image processing and object detecting method, and program storage medium

Patent metadata

Field	Value
Publication number	US-2025308227-A1
Application number	US-202519234637-A
Country	US
Kind code	A1
Filing date	Jun 11, 2025
Priority date	Jun 3, 2014
Publication date	Oct 2, 2025
Grant date	—

Abstract

Provided is an image processing system, an image processing method, and a program for preferably detecting a mobile object. The image processing system includes: an image input unit for receiving an input for some image frames having different times in a plurality of image frames constituting a picture, which is of a pixel on which the mobile object appears or a pixel on which the mobile object does not appear, for selected arbitrary one or more pixels in an image frame at the time of processing; and a mobile object detection model constructing unit for learning a parameter for detecting the mobile object based on the input.

First claim

1 . An image processing system comprising:
at least one memory storing instructions and; at least one processor configured to execute the instructions to: control a displayed screen to display an image captured by a camera; define, based on a first trained model, a segment of the image as a target object; receive a plurality of inputs indicating at least one pixel included in the target object and at least one pixel excluded from the target object on the image including the segment; re-define, based on the plurality of inputs, the segment of the image as the target object; generate training data based on the re-defined segment; and generate, based on the training data, a second trained model for segmentation.

2 . The image processing system according to claim 1 , wherein the plurality of inputs comprises a first input and a second input, the first input indicating the at least one pixel included in the target object, and the second input indicating the at least one pixel excluded from the target object.

3 . The image processing system according to claim 2 , wherein the generating the second trained model comprises generating the second trained model based on a plurality of the training data, each of the plurality of the training data being generated based on each of re-defined segments.

4 . The image processing system according to claim 3 , wherein the re-defined segments are defined on a plurality of images.

5 . The image processing system according to claim 3 , wherein the second trained model employs a neural network, and wherein the generating the second trained model comprises calculating parameters of the neural network based on the training data.

6 . The image processing system according to claim 5 , wherein the at least one processor is configured to execute the instructions to:
detect the target object on the image using the second trained model.

7 . The image processing system according to claim 1 , wherein the processor is further configured to execute the instructions to control the displayed screen to highlight the segment on the image.

8 . An image processing method comprising:
controlling a displayed screen to display an image captured by a camera; defining, based on a first trained model, a segment of the image as a target object; receiving a plurality of inputs indicating at least one pixel included in the target object and at least one pixel excluded from the target object on the image including the segment; re-defining, based on the plurality of inputs, a segment of the image as the target object; generating training data based on the re-defined segment; and generating, based on the training data, a second trained model for segmentation.

9 . The image processing method according to claim 8 , wherein the plurality of inputs comprises a first input and a second input, the first input indicating the at least one pixel included in the target object, and the second input indicating the at least one pixel excluded from the target object.

10 . The image processing method according to claim 9 , wherein the generating the second trained model comprises generating the second trained model based on a plurality of the training data, each of the plurality of the training data being generated based on each of re-defined segments.

11 . The image processing method according to claim 10 , wherein the re-defined segments are defined on a plurality of images.

12 . The image processing method according to claim 10 , wherein the second trained model employs a neural network, and wherein the generating the second trained model comprises calculating parameters of the neural network based on the training data.

13 . The image processing method according to claim 12 , wherein the image processing method comprises
detecting the target object on the image using the second trained model.

14 . The image processing method according to claim 8 , wherein the image processing method further comprises controlling the displayed screen to highlight the segment on the image.

15 . A non-transitory recording medium storing a computer program configured to perform:
controlling a displayed screen to display an image captured by a camera; defining, based on a first trained model, a segment of the image as a target object; receiving a plurality of inputs indicating at least one pixel included in the target object and at least one pixel excluded from the target object on the image including the segment; re-defining, based on the plurality of inputs, a segment of the image as the target object; generating training data based on the re-defined segment; and generating, based on the training data, a second trained model for segmentation.

16 . The non-transitory recording medium according to claim 15 , wherein the plurality of inputs comprises a first input and a second input, the first input indicating the at least one pixel included in the target object, and the second input indicating the at least one pixel excluded from the target object.

17 . The non-transitory recording medium according to claim 16 , wherein the generating the second trained model comprises generating the second trained model based on a plurality of the training data, each of the plurality of the training data being generated based on each of re-defined segments.

18 . The non-transitory recording medium according to claim 17 , wherein the re-defined segments are defined on a plurality of images.

19 . The non-transitory recording medium according to claim 17 , wherein the second trained model employs a neural network, and wherein the generating the second trained model comprises calculating parameters of the neural network based on the training data.

20 . The non-transitory recording medium according to claim 19 , wherein the computer program is configured to perform
detecting the target object on the image using the second trained model.

Assignees

• Nec Corp

Inventors

• Miyano Hiroyoshi

Classifications

• G06T2207/10016

  Video; Image sequence · CPC title

• G06T7/70

  Determining position or orientation of objects or cameras (camera calibration G06T7/80) · CPC title

• G06V20/52

  Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) · CPC title

• G06V10/28

  Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns · CPC title

• G06T7/251

  involving models · CPC title