WO2009096208A1 - Système, procédé et programme de reconnaissance d'objet - Google Patents

Système, procédé et programme de reconnaissance d'objet Download PDF

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Publication number
WO2009096208A1
WO2009096208A1 PCT/JP2009/050126 JP2009050126W WO2009096208A1 WO 2009096208 A1 WO2009096208 A1 WO 2009096208A1 JP 2009050126 W JP2009050126 W JP 2009050126W WO 2009096208 A1 WO2009096208 A1 WO 2009096208A1
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WO
WIPO (PCT)
Prior art keywords
recognition
still image
probability
image
score
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Application number
PCT/JP2009/050126
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English (en)
Japanese (ja)
Inventor
Toshinori Hosoi
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Nec Corporation
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Publication date
Application filed by Nec Corporation filed Critical Nec Corporation
Publication of WO2009096208A1 publication Critical patent/WO2009096208A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects

Definitions

  • the present invention relates to an object recognition system, an object recognition method, and an object recognition program for recognizing an object from an image.
  • a technique for recognizing a category of an object is used in various fields.
  • a category is a term in the pattern recognition field, refers to the classification of patterns, and is sometimes called a class.
  • type and “class” apply.
  • an image is identified as either “automobile” or “not a car”, there are two categories, “automobile” and “not a car”.
  • a template feature vector
  • the category to be recognized can be identified from the image.
  • a pattern consists of all kinds of data including images, sounds and characters.
  • a technique for extracting a partial area that seems to be a predetermined object from a moving image and recognizing the category of the object from the image of this partial area mainly a technique of recognizing based on image variation information in time series, and a moving image
  • Non-Patent Document 1 An example of a method for recognizing a category of an object based on image variation information in time series is disclosed in Non-Patent Document 1.
  • the technique disclosed in Non-Patent Document 1 uses an optical flow direction distribution to recognize an object category from a partial region extracted from a moving image. For example, if the recognition target is a rigid body such as an automobile, the direction of the optical flow is uniform overall, but if the recognition target is a non-rigid body such as a pedestrian, the optical flow is not uniform. Identify both.
  • Non-Patent Document 2 discloses an example of a method for directly recognizing a category of an object from a plurality of pieces of image information by regarding a moving image as a plurality of still images.
  • this Non-Patent Document 2 by using the “constrained mutual subspace method” described in Patent Document 1, moving image face recognition is performed using a recognition algorithm that directly obtains a recognition result from a plurality of data. Yes.
  • This method can be recognized accurately even when the frame rate is low, and further, since it can learn including fluctuations of the object, a high recognition rate can be expected.
  • Patent Document 2 An example of a method for recognizing an object category by recognizing each still image constituting a moving image is Patent Document 2, Patent Document 3, Non-Patent Document 1, Non-Patent Document 2, Non-Patent Document 3 Is disclosed.
  • recognizing a category of an object in a moving image using a recognition method based on still images it is necessary to perform a comprehensive recognition process by integrating individual recognition results for a plurality of still images in time series. .
  • the majority vote method uses a large number of still images in which it is difficult to recognize objects. There was a problem that it could not be recognized.
  • the still image in which the object is difficult to recognize is, for example, “a partial region image in which the object to be recognized cannot be accurately identified” or “a different object is reflected in front of the object to be recognized. ”Image without reflection”, “Image under unexpected illumination fluctuations”, “Image when the posture of the object to be recognized changes significantly”, “Lens distortion, halation, blur, etc. “Images due to image quality fluctuations and noise”.
  • a method of selecting the maximum value among the recognition scores of each still image and using this as an integrated score is also easy.
  • This method can recognize the category of an object almost accurately even when "a number of images in which an object cannot be accurately specified is included in a plurality of partial areas input as recognition targets" Since only the recognition result of a specific still image is used and information on other still images is discarded, excellent performance cannot be obtained.
  • the individual still image recognition rate cannot be 100%, even if an image similar to the object to be recognized is accidentally input, the result that it cannot be identified and is erroneously output is output. It is impossible to avoid accidental misrecognition.
  • each of the general-purpose technologies described above has a problem that there is a situation where it is difficult to recognize an object.
  • a common situation where it is difficult to recognize is “a case where a plurality of images in which an object cannot be accurately specified is included in images of a plurality of partial areas input as recognition targets”.
  • this technique can be generally solved if the maximum score among the recognition scores for each still image is used as the integrated score, but this method is superior because most of the information about still images is discarded. There was a problem that the recognition rate could not be obtained, and a problem that the influence of accidental misrecognition could not be suppressed.
  • FIG. 5 shows an example of a partial area of a recognition target person extracted from each frame image in a moving image of a person, which is caused by a positional shift of the partial area, a size error of the partial area, a change in posture of the recognition target person, and the like.
  • the partial area image that is difficult to recognize is indicated by an arrow.
  • Kanade “A Statistical Method for 3D Object Detection Applied to Faces andCars”, IEEE Conference onComputer Vision and Pattern Recognition, 2000 Japanese Unexamined Patent Publication No. 2000-30065 JP 2005-285011 A Japanese Patent Laid-Open No. 2005-79999
  • An operation in which an integrated score corresponding to a probability that an object in at least n images (n is a natural number equal to or less than the number of all still images) of images is actually a recognition target category is specified in advance.
  • Object recognition system wherein the total score calculation means for calculating according to the equation, that is an object in a moving image on the basis of the total score and a determining means for determining whether a recognition object category.
  • FIG. 4 shows the probability P t1 ( ⁇ c ), in which the still image probability calculation means 130 recognizes each frame image from the still image recognition scores s t1 , s t2 , s t3 ,.
  • the integrated score calculation means 14 calculates “probability that at least one of the plurality of still images is the recognition target category” according to the formula [1] using the respective probabilities obtained for the plurality of still images. Then, an integrated score is calculated based on this probability value (step S150 in FIG. 2, integrated score calculating step).
  • This calculation may be a calculation according to the [Equation 1], a calculation according to the [Equation 2] which is a logarithm of the [Equation 1], or an [Equation 3] or [Equation 4]. It may be calculated according to the formula.
  • Case 1 shown in FIG. 6 is a case where a still image that easily recognizes an object is always input.
  • Case 2 is a case where one still image is extremely difficult to recognize, for example, when extraction of an image region of an object has failed.
  • Case 3 is a case where extraction of an image region of an object has failed with two still images.
  • Case 4 is a case in which it is recognized that the category is generally a recognition target category although it is not easily recognized.
  • Case 5 is a case where it is extremely difficult to recognize most still images.
  • Case 6 is a case where the recognition target category is not generally recognized although it is not easily recognized.
  • Case 2 When the total product of the probabilities that each still image object is the target category is used as the integrated score (the third column in FIG. 6), the case 2 is clearly recognized as the score decreases sharply compared to the case 1 It becomes a value lower than Case 4 that cannot be determined to be the target category. However, in the case of the first embodiment (fifth column in FIG. 6), Case 2 can obtain a higher integrated score than Case 4. This means that, according to the first embodiment, “whether the subject is in the recognition target category can be correctly recognized for a moving image with few scenes where it is difficult to recognize the subject”.
  • the start time t 1 may be the time when the acquisition of the time-series data of the object can be started, or may be a time that is a certain time before the latest time t M. Good. Further, any number of still images (number of frames) from the start time t 1 to the latest time t M can be recognized. In general, the greater the number of still images, the higher the probability that a scene that easily recognizes an object is included, and the recognition rate tends to improve. However, the greater the number of still images, the higher the probability that a feature quantity that is likely to be a recognition target category appears.
  • the determination unit 15 determines whether or not the object shown in the moving image is “human” by performing threshold processing on the integrated score.
  • This threshold may be set based on the result of the experiment. For example, in a system operating environment where an integrated score as shown in FIG. 6 can be obtained, the threshold value may be set to 0.5 so that cases 5 and 6 in FIG. 6 can be correctly rejected.
  • the second embodiment can be applied when there are three or more recognition target categories such as “human” and “non-human”.
  • the identification can be correctly performed even when the number of categories is three or more.
  • a personal computer is used as the data processing device 1 and a semiconductor memory is used as the storage device 2.
  • the identification parameter storage unit 21 and the probability calculation parameter storage unit 22 can be regarded as part of the semiconductor memory.
  • Still picture recognition means 12, still picture probability calculation means 13, integrated score calculation means 14, and result determination means 15 are realized as functions of a CPU of a personal computer.
  • step S210 in FIG. 9 it is determined whether or not the object shown in the moving image is “human”. If the object reflected in the video is determined to be “human”, the final recognition result is terminated as “human”, and if not, whether the object reflected in the video is “automobile” is determined. judge. If it is determined as “automobile”, the final recognition result is ended as “automobile”, otherwise the recognition result is ended as “something that is neither a human nor a car”.
  • the function contents of the still image recognition means 12, the still image probability calculation means 13, the integrated score calculation means 14, and the determination means 15 in the first and second embodiments are programmed and executed by a computer. It may be configured.
  • the present invention can be applied to object monitoring applications such as accurately recognizing a category of an object such as a person or a car from a moving image taken by a camera.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un système de reconnaissance d'objet qui, lors de la réception d'une région partielle qui ressemble à un objet dans une image animée et lors de la reconnaissance d'une catégorie de l'objet, peut reconnaître la catégorie de l'objet dans l'image animée si même une partie d'une image de trame avec laquelle il n'est pas difficile de reconnaître l'objet, est comprise dans l'image animée. Un moyen de reconnaissance d'image fixe (12) effectue une reconnaissance d'image de manière séparée sur une pluralité d'images fixes constituant une image animée et obtient des résultats de reconnaissance d'image fixe. Un moyen de calcul de probabilité d'une image fixe (13) calcule, à partir des résultats de reconnaissance d'image fixe, les probabilités que les objets des images fixes appartiennent à une catégorie cible de reconnaissance. Un moyen de calcul du résultat intégré (14) calcule, à partir des probabilités calculées par le moyen de calcul de probabilité d'une image fixe (13), le résultat intégré correspondant à la probabilité que les objets dans au moins n (n est un nombre naturel inférieur ou égal au nombre total d'images fixes) images parmi les images fixes sont dans la catégorie cible de reconnaissance. Un moyen de détermination (15) détermine, sur la base du résultat intégré, si l'objet dans l'image animée appartient, ou non, à la catégorie cible de reconnaissance.
PCT/JP2009/050126 2008-01-31 2009-01-08 Système, procédé et programme de reconnaissance d'objet WO2009096208A1 (fr)

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JP2008-021832 2008-01-31
JP2008021832 2008-01-31

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012230671A (ja) * 2011-04-22 2012-11-22 Mitsubishi Electric Corp シーンにおけるオブジェクトを分類する方法
JP5644773B2 (ja) * 2009-11-25 2014-12-24 日本電気株式会社 顔画像を照合する装置および方法
WO2020084684A1 (fr) * 2018-10-23 2020-04-30 日本電気株式会社 Système de reconnaissance d'image, procédé de reconnaissance d'image et programme de reconnaissance d'image
WO2020194497A1 (fr) * 2019-03-26 2020-10-01 日本電気株式会社 Dispositif de traitement d'informations, dispositif d'identification personnelle, procédé de traitement d'informations et support de stockage

Citations (6)

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Publication number Priority date Publication date Assignee Title
JPH0620049A (ja) * 1992-06-23 1994-01-28 Japan Radio Co Ltd 侵入者識別システム
JPH09330415A (ja) * 1996-06-10 1997-12-22 Hitachi Ltd 画像監視方法および画像監視システム
JP2004118359A (ja) * 2002-09-24 2004-04-15 Toshiba Corp 人物認識装置、人物認識方法および通行制御装置
JP2004258931A (ja) * 2003-02-25 2004-09-16 Matsushita Electric Works Ltd 画像処理方法、画像処理装置、画像処理プログラム
JP2005354578A (ja) * 2004-06-14 2005-12-22 Denso Corp 対象物体検出・追跡装置
JP2006271657A (ja) * 2005-03-29 2006-10-12 Namco Bandai Games Inc プログラム、情報記憶媒体及び画像撮像表示装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0620049A (ja) * 1992-06-23 1994-01-28 Japan Radio Co Ltd 侵入者識別システム
JPH09330415A (ja) * 1996-06-10 1997-12-22 Hitachi Ltd 画像監視方法および画像監視システム
JP2004118359A (ja) * 2002-09-24 2004-04-15 Toshiba Corp 人物認識装置、人物認識方法および通行制御装置
JP2004258931A (ja) * 2003-02-25 2004-09-16 Matsushita Electric Works Ltd 画像処理方法、画像処理装置、画像処理プログラム
JP2005354578A (ja) * 2004-06-14 2005-12-22 Denso Corp 対象物体検出・追跡装置
JP2006271657A (ja) * 2005-03-29 2006-10-12 Namco Bandai Games Inc プログラム、情報記憶媒体及び画像撮像表示装置

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5644773B2 (ja) * 2009-11-25 2014-12-24 日本電気株式会社 顔画像を照合する装置および方法
JP2012230671A (ja) * 2011-04-22 2012-11-22 Mitsubishi Electric Corp シーンにおけるオブジェクトを分類する方法
WO2020084684A1 (fr) * 2018-10-23 2020-04-30 日本電気株式会社 Système de reconnaissance d'image, procédé de reconnaissance d'image et programme de reconnaissance d'image
JPWO2020084684A1 (ja) * 2018-10-23 2021-09-02 日本電気株式会社 画像認識システム、画像認識方法および画像認識プログラム
WO2020194497A1 (fr) * 2019-03-26 2020-10-01 日本電気株式会社 Dispositif de traitement d'informations, dispositif d'identification personnelle, procédé de traitement d'informations et support de stockage
JPWO2020194497A1 (ja) * 2019-03-26 2021-12-02 日本電気株式会社 情報処理装置、個人識別装置、情報処理方法及び記憶媒体
JP7248102B2 (ja) 2019-03-26 2023-03-29 日本電気株式会社 情報処理装置、個人識別装置、情報処理方法及び記憶媒体

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