JP2020530167A5 - - Google Patents
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- JP2020530167A5 JP2020530167A5 JP2020507521A JP2020507521A JP2020530167A5 JP 2020530167 A5 JP2020530167 A5 JP 2020530167A5 JP 2020507521 A JP2020507521 A JP 2020507521A JP 2020507521 A JP2020507521 A JP 2020507521A JP 2020530167 A5 JP2020530167 A5 JP 2020530167A5
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Description
被写体識別サブシステム２６０２（第１の画像プロセッサとも呼ばれる）は、複数のカメラ１１４から対応する画像シーケンスを受信する被写体画像認識エンジンを含む。被写体画像認識エンジンは、画像を処理して、対応する画像シーケンス内の画像に表される被写体を識別する。一実施形態では、被写体画像認識エンジンが関節ＣＮＮ１１２ａ〜１１２ｎと呼ばれる畳み込みニューラル・ネットワーク（ＣＮＮ）として実装される。重なり合う視野を有するカメラに対応する関節ＣＮＮ１１２ａ〜１１２ｎの出力は、各カメラの２Ｄ画像座標から実空間の３Ｄ座標に関節の位置をマッピングするために組み合わされる。ｊが１〜ｘに等しい被写体（ｊ）毎の関節データ構造８００は、各画像について実空間及び２Ｄ空間における被写体（ｊ）の関節の位置を識別する。被写体データ構造８００の幾つかの詳細を図８に示す。
The subject identification subsystem 2602 (also referred to as the first image processor) includes a subject image recognition engine that receives corresponding image sequences from a plurality of cameras 114. The subject image recognition engine processes the image to identify the subject represented by the image in the corresponding image sequence. In one embodiment, the subject image recognition engine is implemented as a convolutional neural network (CNN) called joints CNN112a-112n. The outputs of the joints CNN112a-112n corresponding to cameras with overlapping fields of view are combined to map the position of the joints from the 2D image coordinates of each camera to the 3D coordinates of real space. The joint data structure 800 for each subject (j) in which j is equal to 1 to x identifies the position of the joint of the subject (j) in the real space and the 2D space for each image. Some details of the subject data structure 800 are shown in FIG.
Claims (15)
各カメラの視野が少なくとも１つの他のカメラの視野と重なり合う複数のカメラを使用して、前記実空間内の対応する視野のそれぞれの画像シーケンスを生成すること、
被写体画像認識エンジンを含む、第１画像プロセッサを使用して、画像を処理して、対応する画像シーケンス内の前記画像に表される被写体を識別すること、
背景画像認識エンジンを含む、第２画像プロセッサを使用して、前記画像シーケンス内の画像内の識別された被写体をマスクしてマスクされた画像を生成し、前記マスクされた画像を処理して、前記対応する画像シーケンス内の前記画像に表される背景変化を識別し且つ分類すること、を備えることを特徴とする方法。 A method of tracking changes within an area of real space,
Using multiple cameras in which the field of view of each camera overlaps the field of view of at least one other camera to generate an image sequence for each of the corresponding fields of view in said real space.
Including the subject image recognition engine, that uses the first image processor, processes the image, to identify the object represented in the image in the image sequence that corresponds,
A second image processor, including a background image recognition engine, is used to mask the identified subject in the image in the image sequence to generate a masked image and process the masked image. A method comprising identifying and classifying background changes represented in the image in the corresponding image sequence.
対応する画像シーケンスの背景画像を格納することと、
画像シーケンス内の画像を処理し、前記マスクされた画像を提供するために、前記識別された被写体を表す前景画像データを、前記対応する画像シーケンスの前記背景画像からの背景画像データで置き換えること、を含む請求項１〜３のいずれか１項に記載の方法。 Using the second image processor
To store the background image of the corresponding image sequence,
Replacing the foreground image data representing the identified subject with background image data from the background image of the corresponding image sequence, in order to process the images in the image sequence and provide the masked image. The method according to any one of claims 1 to 3.
前記画像シーケンス内のＮ個のマスクされた画像のセットを組み合わせて、各カメラのファクタ化画像のシーケンスを生成することと、
前記第２の画像プロセッサが、前記ファクタ化画像のシーケンスを処理することによって、背景変化を識別し且つ分類すること、を含む請求項１〜４のいずれか１項に記載の方法。 Processing the images in the image sequence can
Combining a set of N masked images in the image sequence to generate a sequence of factorized images for each camera.
The method according to any one of claims 1 to 4, wherein the second image processor identifies and classifies background changes by processing the sequence of factorized images.
前記対応する画像シーケンスのための変化データ構造を生成することと、
重なり合う視野を有するカメラのセットからの変化データ構造を処理して、実空間内での前記識別された背景変化の位置を見つけることを、を含み、
前記変化データ構造が、識別された背景変化の前記マスクされた画像内の座標、前記識別された背景変化の在庫商品被写体の識別子、及び、前記識別された背景変化の分類を含む請求項１〜５のいずれか１項に記載の方法。 Using the second image processor
Generating a change data structure for the corresponding image sequence,
Including processing the change data structure from a set of cameras with overlapping fields of view to find the location of the identified background change in real space.
Claims 1 to the change data structure comprising the coordinates of the identified background change in the masked image, the identifier of the identified background change in-stock merchandise subject, and the classification of the identified background change . The method according to any one of 5.
背景変化を識別された被写体に関連付け、前記識別された被写体による在庫商品を取ること、及び前記識別された被写体による在庫商品を棚に置くことの検出を行うことを含む請求項６に記載の方法。 It said change the classification of the background changes identified in the data structure indicates how identification been items are removed or added to the background image,
The method according to claim 6 , wherein the background change is associated with the identified subject, the inventory product by the identified subject is taken, and the inventory product by the identified subject is detected to be placed on the shelf. ..
前記変化の前記位置を前記識別された被写体の手の位置と比較して背景変化を識別された被写体と関連付け、前記識別された被写体による在庫商品を取ること、及び前記識別された被写体による在庫商品を棚に置くことの検出を行うことを含む請求項１〜９のいずれか１項に記載の方法。 Using the first image processor comprises identifying the position of the hand of the identified subject.
The position of the change is compared with the position of the hand of the identified subject, the background change is associated with the identified subject, the inventory product by the identified subject is taken, and the inventory product by the identified subject. The method according to any one of claims 1 to 9, which comprises detecting that the product is placed on a shelf.
前景変化を識別された被写体と関連付け、前記識別された被写体による在庫商品を取ることと、前記識別された被写体による在庫商品を棚に置くことの第２の検出セットを作成すること、及び、
前記第１及び第２の検出セットを処理し、識別された被写体に関する在庫商品のリストを含むログ・データ構造を生成することを含む請求項１〜１１のいずれか１項に記載の方法。 Creating a first detection set of associating a background change with an identified subject, taking inventory of the identified subject, and placing the inventory of the identified subject on a shelf.
Creating a second detection set of associating the foreground change with the identified subject, taking inventory of the identified subject, and placing the inventory of the identified subject on the shelf, and
The method of any one of claims 1-11, comprising processing the first and second detection sets to generate a log data structure containing a list of in-stock items for the identified subject.
前記メモリに、請求項１〜１３のいずれか１項に係る実空間のエリア内における変化を追跡する方法のためのコンピュータ命令がロードされていることを特徴とするシステム。 A system characterized in that a computer instruction for a method of tracking a change in a real space area according to any one of claims 1 to 13 is loaded in the memory.
請求項１〜１３のいずれか１項に係る実空間のエリア内における変化を追跡する方法のためのコンピュータ命令が格納されていることを特徴とする非一時的なコンピュータ可読記憶媒体。 A non-temporary computer-readable storage medium comprising storing computer instructions for a method of tracking changes in an area of real space according to any one of claims 1-13.
Applications Claiming Priority (11)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201762542077P | 2017-08-07 | 2017-08-07 | |
| US62/542,077 | 2017-08-07 | ||
| US15/847,796 | 2017-12-19 | ||
| US15/847,796 US10055853B1 (en) | 2017-08-07 | 2017-12-19 | Subject identification and tracking using image recognition |
| US15/907,112 | 2018-02-27 | ||
| US15/907,112 US10133933B1 (en) | 2017-08-07 | 2018-02-27 | Item put and take detection using image recognition |
| US15/945,466 US10127438B1 (en) | 2017-08-07 | 2018-04-04 | Predicting inventory events using semantic diffing |
| US15/945,473 US10474988B2 (en) | 2017-08-07 | 2018-04-04 | Predicting inventory events using foreground/background processing |
| US15/945,473 | 2018-04-04 | ||
| US15/945,466 | 2018-04-04 | ||
| PCT/US2018/043937 WO2019032306A1 (en) | 2017-08-07 | 2018-07-26 | Predicting inventory events using semantic diffing |
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| Publication Number | Publication Date |
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| JP2020530167A JP2020530167A (en) | 2020-10-15 |
| JP2020530167A5 true JP2020530167A5 (en) | 2021-08-19 |
| JP7191088B2 JP7191088B2 (en) | 2022-12-16 |
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| JP2020507520A Active JP7181922B2 (en) | 2017-08-07 | 2018-07-26 | Predicting inventory events using foreground/background processing |
| JP2020507550A Active JP7208974B2 (en) | 2017-08-07 | 2018-07-26 | Detection of placing and taking goods using image recognition |
| JP2020507669A Active JP7228569B2 (en) | 2017-08-07 | 2018-07-26 | Object identification and tracking using image recognition |
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| JP2020507520A Active JP7181922B2 (en) | 2017-08-07 | 2018-07-26 | Predicting inventory events using foreground/background processing |
| JP2020507550A Active JP7208974B2 (en) | 2017-08-07 | 2018-07-26 | Detection of placing and taking goods using image recognition |
| JP2020507669A Active JP7228569B2 (en) | 2017-08-07 | 2018-07-26 | Object identification and tracking using image recognition |
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| EP (4) | EP3665647A4 (en) |
| JP (4) | JP7191088B2 (en) |
| CA (4) | CA3072062A1 (en) |
| TW (1) | TWI773797B (en) |
| WO (4) | WO2019032306A1 (en) |
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