JP2019527865A - 環境内のコンピュータビジョン駆動型アプリケーションのためのシステムおよび方法 - Google Patents
環境内のコンピュータビジョン駆動型アプリケーションのためのシステムおよび方法 Download PDFInfo
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Abstract
Description
本出願は参照のため本明細書に援用される2016年5月9日出願の米国仮特許出願第62/333,668号からの優先権を主張する。
本発明は、一般的には商行為および在庫管理の分野に関し、具体的には、環境内のコンピュータビジョン駆動型アプリケーションのための新しくかつ有用なシステムおよび方法に関する。
店および倉庫は通常、在庫を管理するチャレンジに直面している。運営的観点から、多くの洗練された事業運営はかなりの時間および資源を在庫の手動計数に注ぎ込む。このようなプロセスは、誤りに脆弱であり、在庫の状態に関する定期的見解を提供するだけである。
本発明の実施形態の以下の説明は、本発明をこれらの実施形態に限定するようには意図されておらず、むしろ当業者が本発明をなし、利用することを可能にするように意図されている。
好適な実施形態の環境内のコンピュータビジョン(CV:computer vision)駆動型アプリケーションのためのシステムおよび方法は、観測空間内のオブジェクトをシームレスに監視、追跡、および考慮するように機能する。本システムおよび方法は好適には、統計ベース手法を介しオブジェクト状態を考慮する撮像システムおよび処理エンジンを利用する。本明細書において説明されるシステムおよび方法は、図1に示すように監視システムの運用能力を変換し、他のシステムとの新しいインテグレーションおよびアプリケーションを可能にし得る。
本システムおよび方法は好適には、環境全体にわたって時間および空間内でオブジェクトを確率的に検知、追跡、およびモデル化するために使用され得る環境オブジェクトグラフ(EOG:environmental object graph)モデリングシステムを適用する。
Claims (44)
- チェックアウトプロセスを促進する方法であって
・環境全体にわたって画像データを収集することと;
・前記画像データから環境オブジェクトグラフを維持することであって、前記環境オブジェクトグラフは、前記環境全体にわたる時間および空間におけるコンピュータビジョン分類型オブジェクトのデータ表現であり、確率的オブジェクト関連性を有するオブジェクトの少なくとも1つのサブセットを含む、維持することと;
・前記環境オブジェクトグラフを維持することは、
・前記画像データ内に捕捉された第1の領域内で、第1のオブジェクトと少なくとも1つの買物客オブジェクトとを分類することと;
・前記第1の領域内で、前記第1のオブジェクトと前記買物客オブジェクト間の相互作用事象を検知することと、
・前記環境オブジェクトグラフを更新することであって、これにより前記第1のオブジェクトは前記買物客オブジェクトに確率的に関連付けられる、更新することとのうちの少なくとも1つのインスタンスを含み:前記方法はさらに、
・前記買物客オブジェクトに確率的に関連付けられたオブジェクトを検査し、これによりチェックアウトリストを生成することとを含む方法。 - 前記画像データ内で捕捉された第2の領域内で、前記買物客オブジェクトを検知し、前記チェックアウトリストによりチェックアウトプロセスを開始することをさらに含む請求項1に記載の方法。
- 前記買物客オブジェクトに関連付けられた口座へアクセスすることをさらに含む請求項2に記載の方法であって、前記チェックアウトプロセスを開始することは前記第2の領域へ入った後に前記チェックアウトリストを前記口座へ課金することを含む、方法。
- 前記チェックアウトプロセスを開始することは、前記チェックアウトリストを前記第2の領域内のチェックアウト処理システムへ伝達することを含む、請求項3に記載の方法。
- 前記チェックアウト処理システムにおいて、前記チェックアウトリストからのアイテムを前記チェックアウト処理システムへ投入することをさらに含む請求項4に記載の方法。
- 前記チェックアウトリスト内のアイテムの前記チェックアウト処理システムの製品入力をバイアスすることをさらに含む請求項5に記載の方法。
- 前記第1のオブジェクトは、初期信頼水準を有する前記買物客オブジェクトへ確率的に関連付けられ;
前記チェックアウトリストを生成することは、信頼性閾値を満足する信頼水準を有するオブジェクトを前記チェックアウトリストへ加えることを含む、請求項2に記載の方法。 - 前記第1の領域は第1のカメラからの画像データ内で捕捉され、前記第2の領域は第2のカメラからの画像データ内で捕捉される、請求項2に記載の方法。
- 前記第1の領域と前記第2の領域は1台のカメラからの画像データ内に捕捉される、請求項2に記載の方法。
- 前記第1のオブジェクトは、第1の信頼水準を有する所有されたオブジェクトとして前記買物客オブジェクトと確率的に関連付けられ、第2のインスタンスにおいて前記環境オブジェクトグラフを維持することは:
・前記画像データ内で捕捉された第3の領域内で、第2のオブジェクトと前記買物客オブジェクトとを分類することと、
・所有されたオブジェクトとしての前記第2のオブジェクトと第2の信頼水準を有する前記買物客オブジェクトとを関連付けることと;
・前記EOGを更新することであって、前記第1の信頼水準は前記第2の信頼水準に応じて少なくとも部分的に変更される、更新することと、を含む、請求項2に記載の方法。 - 前記買物客オブジェクトに関連付けられた口座へアクセスすることと;前記口座のアプリケーションインスタンスにおいて、前記チェックアウトリストを提示することとをさらに含む請求項1に記載の方法。
- 前記第1のインスタンス後の第2のインスタンスにおいて前記環境オブジェクトグラフを維持することは:前記第1の領域において、前記第1のオブジェクトを分類することと、前記環境オブジェクトグラフを更新し、これにより前記第1のインスタンスにおける前記第1のオブジェクトと前記買物客オブジェクトとの関連性を除去することとを含む、請求項1に記載の方法。
- 第2のインスタンスにおいて前記環境オブジェクトグラフを維持することは:
・前記画像データ内で捕捉された第2の領域において、第2のオブジェクトと前記買物客オブジェクトとを分類することであって、前記第2のオブジェクトは確率的に含まれたオブジェクトとの複合オブジェクトである、分類することと、
・前記第2の領域において、前記第1のオブジェクトと前記買物客オブジェクト間の相互作用事象を検知することと、
・前記EOGを更新し、これにより、前記第2のオブジェクトと前記第2のオブジェクトの前記確率的に含まれたオブジェクトは、前記買物客オブジェクトに確率的に関連付けられる、更新することとを含む、請求項1に記載の方法。 - 前記第1のオブジェクトと前記買物客オブジェクト間の前記相互作用事象を検知することは、近接性閾値を満足する前記第1のオブジェクトと前記買物客オブジェクト間の近接性を検知することを含む、請求項1に記載の方法。
- 前記第1のオブジェクトと前記買物客オブジェクトとを分類することは、分類中にコンピュータビジョン駆動型プロセスを適用することであって、前記コンピュータビジョン駆動型プロセスは、少なくとも画像特徴抽出および分類とニューラルネットワークのアプリケーションとを含む、適用することを含む、請求項1に記載の方法。
- ・環境全体にわたって画像データを収集することと;
・前記画像データから環境オブジェクトグラフを維持することとを含む方法において、前記環境オブジェクトグラフを維持することは、
・オブジェクトを分類し、対応オブジェクトインスタンスを環境オブジェクトグラフ内に保管することと、
・オブジェクト位置を追跡し、オブジェクト経路内のオブジェクトインスタンスの関連性を確立することと、
・相互作用事象を検知し、検知された相互作用事象のサブセットの事象インスタンスに関し、前記相互作用事象に関与する少なくとも2つのオブジェクトインスタンスのオブジェクト関連性を生成することと、
・オブジェクト関連性全体にわたって少なくとも1つのオブジェクトインスタンスにおける変更を伝播することを含み前記環境オブジェクトグラフを更新することと;
を含む繰り返しプロセスであり、前記方法はさらに、
・前記環境オブジェクトグラフ内の少なくとも1つのオブジェクトインスタンスのオブジェクト状態を検査し、前記オブジェクト状態に関連付けられた行為を実行することを含む、方法。 - 画像データを収集することは、環境全体にわたって分散された複数の画像捕捉装置から画像データを収集することを含む、請求項16に記載の方法。
- 環境全体にわたって分散された複数の画像捕捉装置から画像データを収集することは、在庫保管捕捉構成、相互作用捕捉構成、オブジェクト識別捕捉構成、および可動捕捉構成から選択される少なくとも2つの画像捕捉構成を含む一組の画像捕捉装置から画像データを収集することを含む、請求項17に記載の方法。
- 前記相互作用事象を検知することは、第1の分類の第1のオブジェクトと、近接性閾値を満足する第2の分類の少なくとも1つの第2のオブジェクトとの間の近接性を検知することを含む、請求項16に記載の方法。
- 前記相互作用事象を検知することは:少なくとも1つの相互作用事象に関して、オブジェクト近接事象を検知することと、少なくとも1つの第2の相互作用事象に関して、オブジェクト変換事象を検知することとを含む、請求項16に記載の方法。
- 前記オブジェクト変換事象を検知することは、オブジェクト出現事象、オブジェクト消失事象、およびオブジェクト分類変更事象の組から選択されたオブジェクト変換を検知することを含み得る、請求項20に記載の方法。
- オブジェクトを分類することは、少なくとも1つの製品オブジェクト、複合オブジェクト、および人オブジェクトを含む一組の潜在的オブジェクトタイプのうちの1つとしてオブジェクトを識別することを含み得る、請求項16に記載の方法。
- 前記環境オブジェクトグラフを維持することはさらに、前記環境オブジェクトグラフ内のオブジェクトインスタンスを、少なくとも1つのオブジェクトとの階層的関連性を確率的に有するとしてインスタンス化することを含む、請求項16に記載の方法。
- 前記環境オブジェクトグラフを維持することはさらに、オブジェクトインスタンスの少なくとも2つの確率的に可能な状態をインスタンス化することを含む、請求項16に記載の方法。
- オブジェクトを分類することは、第1の組の候補オブジェクトの初期分類モデルを介しオブジェクトを分類することと、前記初期分類モデルを介した前記オブジェクトの分類が分類条件を満たさなければ第2の組の候補オブジェクトの少なくとも第2の拡張分類モデルを介し前記オブジェクトを分類することとを含む、請求項16に記載の方法。
- オブジェクトを分類することは、購買パターン、顧客購入履歴、および製品価格の組から選択された補足入力を取り出すことと;前記補足入力のうちの1つに少なくとも部分的に基づきオブジェクトを分類することとを含む、請求項16に記載の方法。
- 画像データを収集することは、チェックアウト処理システムにおいて画像データを収集することと;前記環境オブジェクトグラフを更新する際に補足入力としてPOS入力により生成される製品識別子を適用することとを含む、請求項16に記載の方法。
- オブジェクト状態を検査して行為を実行することは:
前記環境オブジェクトグラフ内の低信頼性シナリオを検知することと;
前記環境の少なくとも1つの領域を規定する環境オブジェクトグラフ校正要求を伝達することと;
前記領域から入力された校正データを受信することと、
前記校正データ入力により前記環境オブジェクトグラフを更新することとを含み得る、請求項16に記載の方法。 - オブジェクト状態を検査することは、買物客関連オブジェクトインスタンスに関連付けられた製品オブジェクトインスタンスのチェックアウトリストを生成することを含む、請求項16に記載の方法。
- 行為を実行することは:前記買物客関連オブジェクトインスタンスに関連付けられた口座へアクセスすることと、前記買物客関連オブジェクトインスタンスがチェックアウト領域内で検知されると前記チェックアウトリストを前記口座へ課金することとを含む、請求項29に記載の方法。
- 行為を実行することは、前記買物客関連オブジェクトインスタンスの近傍のチェックアウト処理システムへ前記チェックアウトリストを伝達することを含む、請求項29に記載の方法。
- 行為を実行することは、前記環境オブジェクトグラフを介し少なくとも部分的に駆動される在庫管理システムを提供することを含み、
前記在庫管理システムを提供することは:製品の保管寿命を報告することと、製品計数を報告することと、製品位置を報告することと、コンテナ内容を報告することとを含む、請求項16に記載の方法。 - 一組のメディアコンテンツセグメントについて、
前記環境オブジェクトグラフ内のモデリング信頼性を判断すること;
前記モデリング信頼性に基づき内容表現を選択することであって、前記一組の選択肢から選択された前記内容表現は少なくとも1つのモデル化データ表現とメディア表現とを含む、選択することと;
前記内容表現を保管することとをさらに含む請求項16に記載の方法。 - 少なくとも1つの条件と行為とによりオブジェクト規則を構成することをさらに含む請求項16に記載の方法であって、オブジェクト状態を検査して行為を実行することは、前記オブジェクト規則の発生を検知することと前記オブジェクト規則の前記行為をトリガすることとを含む、方法。
- オブジェクトを分類することは、少なくとも画像特徴抽出および分類と畳み込みニューラルネットワークとを含むコンピュータビジョン駆動型プロセスを適用すること、を含む、請求項16に記載の方法。
- ・環境内の画像データを収集するように構成された撮像システムと;
・前記画像データから環境オブジェクトグラフを維持するように構成された処理エンジンであって、前記環境オブジェクトグラフは、前記環境全体にわたる時間および空間における分類型オブジェクトのデータ表現であり、確率的オブジェクト関連性を有するオブジェクトの少なくとも1つのサブセットを含む、エンジンとを含むシステムにおいて、
・前記処理エンジンはさらに:
・前記画像データ内のオブジェクトを分類し、対応オブジェクトインスタンスを前記環境オブジェクトグラフ内に保管し、
・オブジェクト位置を追跡し、オブジェクト経路内のオブジェクトインスタンスの関連性を確立し、
・相互作用事象を検知し、検知された相互作用事象のサブセットの事象インスタンス毎に、前記相互作用事象に関与する少なくとも2つのオブジェクトインスタンスのオブジェクト関連性を生成し、
・前記環境オブジェクトグラフを更新し、少なくとも1つのオブジェクトインスタンスの変更をオブジェクト関連性全体にわたって伝播するように構成される、システム。 - 前記撮像システムが、在庫保管捕捉構成の画像捕捉装置、相互作用捕捉構成の画像捕捉装置、オブジェクト識別捕捉構成の画像捕捉装置、および可動捕捉構成の画像捕捉装置を含む請求項36に記載のシステム。
- 前記処理エンジンはさらに:前記環境オブジェクトグラフ内の第1の組のオブジェクトインスタンスを複合オブジェクトモデリングによりインスタンス化し、前記環境オブジェクトグラフ内の第2の組のオブジェクトインスタンスを確率的マルチステートモデリングによりインスタンス化するように構成される、請求項36に記載のシステム。
- 前記環境オブジェクトグラフ内の買物客オブジェクトインスタンスに関連付けられたオブジェクトインスタンスを検査し、製品のチェックアウトリストを生成するように構成された仮想カート管理システムをさらに含む請求項36に記載のシステム。
- 前記仮想カート管理システムはさらに、前記買物客関連オブジェクトインスタンスがチェックアウト領域において検知されると前記買物客オブジェクトインスタンスに関連付けられた口座へアクセスし、前記チェックアウトリストを前記口座へ課金するように構成される、請求項39に記載のシステム。
- チェックアウト処理システムをさらに含む請求項39に記載のシステムであって、前記チェックアウトリストは前記チェックアウト処理システムへ伝達される、システム。
- 前記環境オブジェクトグラフへの検査インターフェースを含む在庫管理システムをさらに含む請求項36に記載のシステム。
- 前記環境オブジェクトグラフと連携して動的メディアコンテンツ表現を保管するように構成されたメタメディア保管システムをさらに含む請求項36に記載のシステム。
- 校正データ入力を前記処理エンジンへ提供するように構成された校正ツールをさらに含む請求項36に記載のシステム。
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