DETECTION DEVICE, DETECTION PROGRAM, DETECTION METHOD, VEHICLE EQUIPPED WITH DETECTION DEVICE, PARAMETER CALCULATION DEVICE, PARAMETER CALCULATING PARAMETERS, PARAMETER CALCULATION PROGRAM, AND METHOD OF CALCULATING PARAMETERS

• US 20170098123A1
• Filed: 12/15/2016
• Published: 04/06/2017
• Est. Priority Date: 05/28/2014
• Status: Abandoned Application

• Current Assignee: DENSO Corporation
• Original Assignee: DENSO Corporation
• Inventors: YOKOI, Kensuke, TAMATSU, Yukimasa, SATO, Ikuro
• Application Number:
  US15/379,524
• Publication Number:
  US 20170098123A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06V 10/454 Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G06V 40/103 Static body considered as a whole, e.g. static pedestrian or occupant recognition
• CPC Class Codes: H04N 5/144 Movement detection for video coding H04N19/503; analysis of motion in general G06T7/20

Abstract

A detection device has a neural network process section performing a neural network process using parameters to calculate and output a classification result and a regression result of each of frames in an input image. The classification result shows a presence of a person in the input image. The regression result shows a position of the person in the input image. The parameters are determined based on a learning process using a plurality of positive samples and negative samples. The positive samples have segments of a sample image containing at least a part of the person and a true value of the position of the person in the sample image. The negative samples have segments of the sample image containing no person.

Claims

1. A parameter calculation device capable of performing learning of a plurality of positive samples and negative samples, in order to calculate parameters for use in a neural network process of an input image, wherein each of the positive samples comprises a set of a segment of a sample image containing at least a part of the person and a true value of the position of the person in the sample images, and each of the negative samples comprises a segment of the sample image containing no person.

2. A parameter calculation program, to be executed by a computer, of performing a function of a parameter calculation device capable of performing learning of a plurality of positive samples and negative samples, in order to calculate parameters for use in a neural network process of an input image,

wherein each of the positive samples comprises a set of a segment of a sample image containing at least a part of the person and a true value of the position of the person in the sample images, and each of the negative samples comprises a segment of the sample image containing no person.

3. A method of calculating parameters for use in a neural network process of an input image, by performing learning of a plurality of positive samples and negative samples, where each of the positive samples comprises a set of a segment of a sample image containing at least a part of the person and a true value of the position of the person in the sample images, and each of the negative samples comprises a segment of the sample image containing no person.