JP2007052609A - Hand area detection device, hand area detection method and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a hand area extraction device and a hand area detection method, which can be used in actual environment without depending on an individual difference of a person or an illumination change. <P>SOLUTION: This hand area detection device has: an image capturing part 12 capturing an image imaged by an imaging part 11; a flesh color candidate area selection part 13 selecting a flesh-colored candidate area from the image captured by the image capturing part 12; a hand candidate area extraction part 14 extracting a hand candidate area from the flesh-colored candidate area selected by the flesh color candidate selection part; a shape complexity calculation part 15 calculating a complexity of a shape of the hand candidate area extracted by the hand candidate area extraction part; a complexity evaluation part 16 evaluating the complexity calculated by the shape complexity calculation part 15; and a hand area determination part 17 determining a hand area on the basis of an evaluation result by the complexity evaluation part 16. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an interface between a person and a device, and more particularly to a hand area detecting device, a hand area detecting method, and a program for detecting and recognizing an area of a human hand.

  In order to realize a natural interface between humans and devices, information such as human gestures and gestures, including various actions and voices of the operator, is analyzed in an integrated manner, and the presence and intention of humans are determined. It is essential to detect and recognize. Therefore, various techniques related to human behavior analysis have been studied. These can be broadly classified as those that focus on human faces (for example, face orientation, facial expression, line of sight, lip movement, etc.) and human limb movements (hand gestures, pointing, gestures, motion events, walking patterns, etc.) ). Among them, detection and recognition of the hand region in a general environment is one of the most important technologies, and many studies have been made. For example, a hand shaking detection method focusing on a reciprocating motion that moves a human hand to the left and right (for example, see Non-Patent Document 1), a method for attaching a special object such as a data glove (for example, see Non-Patent Document 2), a person Have been proposed (for example, see Non-Patent Document 3 and Non-Patent Document 4).

Irie, Umeda: Detection of hand gestures from images using time-series changes in gray values, P2-8, Symposium on Image Recognition and Understanding (MIRU'02) (Nagoya Institute of Technology (Nagoya City), July 30-August 1st, 2002) Hoshino: Design of robot hand capable of non-verbal communication, Ministry of Education, Culture, Sports, Science and Technology Grant-in-Aid for Scientific Research “Informatics research that pioneers the foundation of IT deepening” Research item A03 “Research on human information processing and its application” No. 4 Kaishira Kenkyukai (Shibaran Kaikan (Kyoto City), July 2, 2003) Ito, Ozeki, Nakamura, Ota: Robust recognition and tracking of hands and grasped objects for video indexing, Symposium on Image Sensing, pp.143-162, (2003) Irie, Umeda: Construction of intelligent room based on gesture recognition, 4th rotating image processing practical use workshop, March 6-7, 1995

  Although the hand shaking detection method based on the periodic movement of the hand described in Non-Patent Document 1 has an advantage that it is not easily affected by illumination or the like, the hand shake detection method may be affected by a difference in brightness from the background or an individual difference in hand shaking. There is a possibility that the area cannot be accurately detected.

  Moreover, although the method using a special thing like the data glove described in the said nonpatent literature 2 can detect a hand reliably, it is not a natural interface.

  The hand region detection methods using only the human skin color described in Non-Patent Documents 3 and 4 are widely used. However, the lighting environment changes, individual differences in skin color, camera internal settings (for example, white In some cases, it is difficult to detect the hand region due to the balance.

  Also, a hand region detection method using a multi-sensor for detecting skin color, hand movement, hand size, surface temperature of human skin, etc. has been proposed, but the construction of the system becomes complicated. .

  The present invention has been made to solve the above-described problems. A hand region extraction device, a hand region detection method, and a program that can be used in a real environment without depending on individual differences or changes in lighting. The purpose is to provide.

  In order to solve the above-described problem, a hand region detection device according to the present invention includes a hand candidate detection unit that detects a hand candidate region from an input image based on a person's statistical skin color feature, and the hand candidate detection unit. Shape complexity calculating means for calculating the shape complexity of the extracted hand candidate area, and a hand area for detecting the hand area from the hand candidate area based on the shape complexity calculated by the shape complexity calculating means And a detecting means.

  The hand region detection method according to the present invention includes a hand candidate detection step for detecting a hand candidate region based on a person's statistical skin color feature from an input image, and a hand candidate region extracted in the hand candidate detection step. A shape complexity calculating step for calculating the shape complexity of the hand, and a hand region detecting step for detecting a hand region from the candidate regions of the hand based on the shape complexity calculated in the shape complexity calculating step. It is a feature.

  Further, the program according to the present invention is a program for executing a process of detecting a hand region from an input image. The hand candidate detecting step of detecting a hand candidate region from the input image based on a human skin color feature; A shape complexity calculation step for calculating the shape complexity of the hand candidate region extracted in the candidate detection step, and a hand region from the hand candidate region based on the shape complexity calculated in the shape complexity calculation step. And a hand region detecting step for detecting.

  According to the present invention, a hand candidate area is extracted from an input image based on a human skin color characteristic, a shape complexity of the extracted hand candidate area is calculated, and a hand candidate area is calculated based on the shape complexity. By detecting the hand area from the camera, it can be used in an actual environment that does not depend on individual differences or lighting changes.

  Hereinafter, specific embodiments of the present invention will be described in detail with reference to the drawings. FIG. 1 is a block diagram illustrating a configuration of a hand region detection device according to the first embodiment. The hand region detection device 10 includes an imaging unit 11, an image capturing unit 12 that captures an image captured by the imaging unit 11, and a skin color candidate region that selects a skin color candidate region from the image captured by the image capturing unit 12. The selection unit 13, the hand candidate region extraction unit 14 that extracts a hand candidate region from the skin color candidate region selected by the skin color candidate selection unit 13, and the complexity of the shape of the hand candidate region extracted by the hand candidate region extraction unit 14 A shape complexity calculating unit 15 for calculating the degree and a hand region detecting unit 16 for detecting a hand region based on the complexity calculated by the shape complexity calculating unit 15 are configured.

  The imaging unit 11 includes a camera having an imaging element such as a CCD (Charge Coupled Device).

  The image capturing unit 12 captures an image in a format such as JPEG (Joint Photographic Experts Group) from the imaging unit 11.

  The skin color candidate region selection unit 13 selects a skin color candidate region by determining whether each pixel in the image is the color of the skin color model using a skin color model based on human statistical skin color characteristics. This skin color model is obtained by statistical processing using hue, saturation, RGB information, and the like.

  The hand candidate area extraction unit 14 calculates, for example, image distribution characteristics such as hue and saturation distribution for each area selected by the skin color candidate area selection unit 13, and includes, for example, hue and saturation in those areas. A region having the largest pixel having similar color characteristics such as degree is extracted as a hand candidate region.

  For each hand candidate area extracted by the hand candidate area extraction unit 14, the shape complexity calculation unit 15 is based on, for example, the ratio of the area (number of pixels) to the peripheral length or the distance from the center of the hand candidate area. To calculate the shape complexity.

  The hand region detection unit 16 detects the hand region by comparing and evaluating the complexity of each hand candidate region calculated by the shape complexity calculation unit 15 with, for example, a preset threshold value.

  In the configuration of the hand region detection device 10 described above, an image is acquired from the imaging unit 11, but an image may be acquired from various media such as a DVD (Digital Versatile Disc) and the Internet.

  Next, processing for detecting a hand region from an input image will be described with reference to the drawings. 2 to 5 are diagrams showing examples of images when skin color candidates are selected from an input image using RGB information.

  The skin color candidate region selection unit 13 takes the document (MJ Jones and JM Rehg: Statistical color models with application to skin detection, Proc. IEEE Conf.) For the input image shown in FIG. Using the RGB information of each pixel described in Computer Vision and Pattern Recognition, vol. 2, pp. 274-280, 1999.), the skin color model color is detected as shown in FIG. Then, by determining whether each pixel is a skin color model color, skin color candidate areas A to D are selected as shown in FIG. Thereby, skin color candidate areas A to D as shown in FIG. 5 can be determined for the input image shown in FIG. In addition to the RGB information, for example, by using statistical information (a priori knowledge) of skin color obtained by learning, it is determined whether each pixel is skin color, and region integration using image filtering processing A skin color candidate region in the image may be selected by the processing.

  The hand candidate area extraction unit 14 calculates a hue image Hue (i, j) and a saturation image Sta (i, j) from the input image as shown in FIG. 2, and converts the input image into a hue image and a saturation image. To do. The hue represents the position of the color on the spectrum, and is represented by an angle of 0 degrees to 360 degrees. The saturation represents the vividness of the color. The value decreases as the color approaches gray, and the value increases as the color approaches the primary color.

The hand candidate area extraction unit 14 also outputs a hue distribution (histogram) h _m (k) and a saturation distribution (histogram) s _m (for each skin color candidate area A to D selected by the skin color candidate area selection unit 13. k) (k = 0, 1, 2,..., K) are calculated by the equations (1) and (2), respectively.

Here, R _m represents each skin color complementary region, and m (m = 1, 2,..., M) is the number of skin color candidate regions. Also, g (Hue (i, j)) and g (Sta (i, j)) are respectively in the pixel (i, j) of the hue image Hue (i, j) and the saturation image Sta (i, j). Indicates the luminance value. Further, δ [x] is 1 when x is 0, and 0 when x is not 0.

For example, when the hue distribution h _m (k) of the skin color candidate region B of the hue image shown in FIG. 6 is calculated using the above equation (1), a histogram as shown in FIG. 7 can be obtained. Moreover, for example, when the saturation distribution s _m (k) of the skin color candidate region B of the saturation image shown in FIG. 8 is calculated using the above equation (2), a histogram as shown in FIG. 9 can be obtained. .

  FIGS. 10 and 11 are examples of flesh color hue regions and saturation regions detected from the above histogram. FIGS. 12 and 13 are a hue image and a saturation image in which hand candidate areas are detected based on the hue area and the saturation area of the skin color, respectively.

From the hue distribution h _m (k) and the saturation distribution s _m (k) of each skin color candidate area A to D, the hand candidate area extraction unit 14 has a hue value with the largest number of pixels as shown in FIGS. The h_Level and the saturation value s_Level and their variances h_var and s_var are detected.

  Here, since the flesh color has a low hue value, the hue value in the hue image Hue (i, j) of each flesh color candidate area A to D is within h_Level ± h_var, and the value is the flesh initial hue value H_Initial. The smaller pixel area is defined as a flesh-colored hue area (image) Hm (i, j). As a result, the skin tone area Hm (i, j) of each skin color candidate area A to D as shown in FIG. 12 is obtained. In the skin color candidate areas A to D shown in FIG. 12, the skin color hue area Hm (i, j) is shown in black.

  Further, since the skin color has a high saturation value, the saturation value in the saturation image Sta (i, j) of each skin color candidate region A to D is within s_Level ± s_var, and the value is the saturation of the skin color. A pixel region larger than the initial value S_Initial is defined as a skin color saturation region (image) Sm (i, j). Thus, the skin color saturation region Sm (i, j) of each skin color candidate region A to D as shown in FIG. 13 is obtained. In the skin color candidate areas A to D shown in FIG. 13, the skin color saturation area Sm (i, j) is shown in white.

  A pixel region where the skin tone region Hm (i, j) and the saturation region Sm (i, j) overlap is defined as a hand candidate region Rm. That is, like the hand candidate area shown in FIG. 14, in each skin color candidate area A to D, the black area shown in FIG. 12 and the white area shown in FIG. 13 overlap.

  In this way, by extracting the region where the pixels with the same hue and saturation are the largest, that is, the region indicating the dominant color, as the candidate region of the hand, it is possible to cope with individual differences in human skin color and lighting changes in the real environment. Robustness can be improved.

  The shape complexity calculation unit 15 calculates the complexity of the hand candidate region based on, for example, the area (number of pixels) and the peripheral length of the hand candidate region Rm extracted by the hand candidate region extraction unit 14. .

  Here, L is the perimeter of the hand candidate region, S is the area of the hand candidate region, and δ is the complexity of the shape.

  The hand region detection unit 16 compares and evaluates the complexity of the shape of each hand candidate region calculated by the shape complexity calculation unit 15 with the complexity corresponding to a predetermined hand shape. FIG. 15 is an example of the calculation (learning) result of the complexity according to the hand shape. For example, the shape complexity in the state where five fingers are open, that is, the state where the hand is open (K1) is significantly different from the shape complexity in the state where the hand is not open (K6). Considering this, a threshold value for detecting the hand region is determined empirically. For example, the shape complexity in a state where four or more fingers are open (δ> 0.9) is set as the threshold value of the hand region so that the head region is not detected as the hand region. A case where the complexity of the hand candidate area is larger than a predetermined threshold is detected as a hand area.

  FIG. 16 shows an image in which five fingers are not opened, that is, a hand is not opened, and FIG. 17 shows the shape complexity of the hand candidate region at that time. When the image shown in FIG. 16 is input, the hand region detection unit 16 compares the complexity of the hand candidate regions b and c with a threshold value. For example, when the complexity threshold is set as δ = 0.9, the complexity of both the hand candidate area b and the hand candidate area c is lower than the threshold, so the hand area detection unit 16 detects these as hand areas. do not do.

  FIG. 18 shows a state in which the two fingers, the index finger and the middle finger, are open, and FIG. 19 shows the shape complexity of the hand candidate region at that time. Even when an image with two fingers open as shown in FIG. 18 is input and the complexity threshold is set to δ = 0.9, the complexity of both the hand candidate area b and the hand candidate area c is the threshold. Therefore, the hand region detection unit 16 does not detect these as hand regions.

  20 shows a state where all five fingers are open, that is, a state where the hand is open, and FIG. 21 shows the shape complexity of the hand candidate region at that time. As shown in FIG. 20, when an image with an open hand is input and the threshold value of the complexity of the shape of the hand candidate region is set to δ = 0.9, the complexity of the hand candidate region c is greater than the threshold value. Since the complexity of the hand candidate area b is smaller than the threshold value, the hand area detection unit 16 detects the hand candidate area b as a hand area.

  Note that, as described above, not only the complexity is calculated based on the area and the peripheral length of the hand candidate area, but the center of the hand candidate area is detected based on the distance from the center as shown in FIG. You may calculate. In this case, the distance function d (θ) may be defined as in equation (4).

  FIG. 23 is a graph of the distance function according to Equation (4). Here, when the maximum value of the distance function d (θ) is equal to or greater than the threshold (Thd) indicating the shape of the finger, the number of maximum values can be estimated as the number of fingers.

  As shown in FIG. 23, the hand region detection unit 16 detects a hand region according to the number of local maximum values equal to or greater than a threshold value (Thd). For example, when the threshold of the number of maximum values equal to or greater than the threshold (Thd) is 4, that is, when four or more fingers are detected, the hand candidate area is detected as a hand area.

  Also, as shown in FIG. 23, h (k) (k = 1, 2,..., K) may be defined, and if h (k) is equal to or greater than a certain threshold value, it may be estimated as a finger. In this case, the hand region detection unit 16 detects the hand region according to h (k) equal to or greater than the threshold value. For example, when the threshold value is 5, when five fingers are detected, the hand candidate area is detected as a hand area. Note that the threshold for detecting these fingers and the threshold for detecting the hand region may be determined by learning.

  In this way, selecting candidate skin color regions based on human statistical skin color characteristics, extracting hand candidate regions based on image distribution characteristics within those regions, and evaluating the shape complexity of these hand candidate regions Thus, robust hand region detection can be performed even in a real environment without depending on individual differences in human skin color or lighting changes.

  In addition, since the region feature when the hand is open can be distinguished from other region features by evaluating the shape complexity, robust hand region detection can be performed.

  In the present embodiment, skin color candidate regions based on statistical skin color features are selected, and hand candidate regions based on image distribution characteristics in those regions are extracted. However, the present invention is not limited to this. Alternatively, skin candidate region selection and hand candidate region extraction may be integrated, and the hand candidate regions may be narrowed down by performing RGB processing, image distribution processing, and the like in an arbitrary order.

  FIG. 24 is a block diagram illustrating a configuration of a hand region detection device according to the second embodiment. In order to further improve the robustness of the real environment, the hand region detection device 20 of this embodiment further moves with respect to the detected hand region using, for example, difference information between frames (region). ) As the correct hand area. In addition, the same code | symbol is attached | subjected to the structure same as the hand region detection apparatus 10 in 1st Embodiment, and description is abbreviate | omitted.

  The hand region detection device 20 includes an imaging unit 11, an image capturing unit 12 that captures an image captured by the imaging unit 11, and a skin color candidate region that selects a skin color candidate region from the image captured by the image capturing unit 12. The selection unit 13, the hand candidate region extraction unit 14 that extracts a hand candidate region from the skin color candidate region selected by the skin color candidate selection unit 13, and the complexity of the shape of the hand candidate region extracted by the hand candidate region extraction unit 14 A shape complexity calculating unit 15 for calculating the degree, a hand region detecting unit 16 for detecting a hand region based on the complexity calculated by the shape complexity calculating unit 15, and a hand region detected by the hand region detecting unit 16 A delay circuit unit 21 that delays an image including a predetermined frame, a motion region detection unit 22 that compares the image delayed by a predetermined frame by the delay circuit unit 21 with an image of the current frame, and detects a motion region; Motion region detector 2 In constructed and a hand region determining unit 23 for determining the hand region based on the detected motion region.

  The delay circuit unit 21 has a buffer that temporarily holds an input image for a predetermined frame. For example, the delay circuit unit 21 receives the input image and an image one frame before that in the motion region detection unit 22. Output.

  For example, the motion region detection unit 22 compares two input images that differ by one frame and detects a motion region. Here, it is preferable that the motion region detection unit 22 detects the motion in the vicinity of the hand region detected by the hand region detection unit 16.

  The hand region determination unit 23 determines whether or not the motion region detected by the motion region detection unit 22 is a hand region detected by the hand region detection unit 16, and only when the detected motion region is a hand region. The area is determined as a hand area. It is determined whether or not the hand region detected by the hand region detection unit 16 is a motion region detected by the motion region detection unit 23. If the detected hand region is a motion region, the hand region is determined as a hand region. May be.

  Next, the operation of the hand region detection device 20 in the second embodiment will be described.

  Since the hand region detection device 20 detects the hand region by the hand region detection unit 16 in the same manner as the hand region detection device 10 in the first embodiment, a detailed description thereof will be omitted. As described above, the hand region detection device 20 selects a skin color candidate region based on a person's statistical skin color feature from the input image, extracts a hand candidate region based on image distribution characteristics in these regions, and extracts the hand region. The hand region is detected based on the shape complexity of the candidate region. The image in which the hand area is detected is input to the delay circuit unit 21.

  25 and 26 show examples of images with different imaging times output from the delay circuit unit 21, respectively. FIG. 27 is an image example in which a motion region is detected based on the images of FIGS. 25 and 26 output from the delay circuit 21.

  The delay circuit unit 21 holds the input image in a buffer, and outputs, for example, an image Im (t) as shown in FIG. 25 and an image Im (t + 1) as shown in FIG. .

  The motion region detection unit 22 detects motion information from the input image Im (t) and the image Im (t + 1), for example, by inter-frame difference processing. Thereby, as shown in FIG. 27, a hand part and a face part can be detected.

  The hand region determination unit 23 determines whether or not the detected motion region is a hand region detected by the hand region detection unit 16. For example, as shown in FIG. 27, the hand part that is the hand area detected by the hand area detection unit 16 is determined as the final hand area among the hand part and the face part in which the motion area is detected (see FIG. 28). ).

  FIG. 29 shows an example of selecting a skin color candidate area, and FIG. 30 shows an example of detecting a hand area. By using the motion information described above, as shown in FIG. 29, the skin color candidate regions G and H depicted on the poster from the hand regions shown in the skin color candidate regions E, F, G, and H selected by the skin color candidate selection unit 13 The hand region can be removed, and the final hand region can be reliably detected as shown in FIG.

  On the other hand, when the motion information is not used, as shown in FIG. 31 and FIG. 32, the hand regions in the skin color candidate regions E and G are finally detected from the skin color candidate regions E, F, G, and H. . That is, the hand depicted on the poster is also detected as a hand region.

  In the second embodiment described above, after detecting the hand region based on the complexity, the motion region is detected, and the final hand region is determined from the hand region and the motion region. A hand region may be determined by detecting a motion region from an input image from the image capturing unit 12.

  FIG. 33 is a block diagram illustrating a configuration of a hand region detection device according to the third embodiment. In order to further improve the robustness of the real environment, the hand region detection device 30 of this embodiment estimates distance information using, for example, a stereo camera, and removes skin color candidate regions that are more than a predetermined distance from the background. . In addition, the same code | symbol is attached | subjected to the structure same as the hand region detection apparatus 10 in 1st Embodiment, and description is abbreviate | omitted.

  The hand region detection device 30 includes an imaging unit 11, an image capturing unit 12 that captures an image captured by the imaging unit 11, and a skin color candidate region that selects a skin color candidate region from the image captured by the image capturing unit 12. The selection unit 13, the hand candidate region extraction unit 14 that extracts a hand candidate region from the skin color candidate region selected by the skin color candidate selection unit 13, and the complexity of the shape of the hand candidate region extracted by the hand candidate region extraction unit 14 A shape complexity calculating unit 15 for calculating the degree, a hand region detecting unit 16 for detecting a hand region based on the complexity calculated by the shape complexity calculating unit 15, a reference imaging unit 31, an imaging unit 11, and a reference Based on the distance information acquisition unit 32 that acquires the distance information of the subject in the image based on the imaging unit 32, the hand region detected by the hand region detection unit 16, and the distance information acquired by the distance information acquisition unit 32. Hand region determination to determine hand region Configured with a 33.

  The reference imaging unit 31 is configured by a camera having an imaging element such as a CCD, and is configured as a stereo camera using the imaging unit 11. In addition, it is preferable to further configure a stereo camera with a plurality of cameras.

  The distance information acquisition unit 32 uses the imaging unit 11 and the reference imaging unit 31 to measure the distance to the object based on the “stereo method”, for example. The stereo method referred to here is a three-dimensional view of each point in a scene, that is, a captured image using images captured from the viewpoints (projection centers) of a plurality of cameras having a predetermined positional relationship based on the principle of so-called “triangulation”. Estimate the position in space and measure the distance to the projection center.

  The hand region determination unit 33 determines the final hand region based on the distance information acquired by the distance information acquisition unit 32 for the hand region detected by the hand region detection unit 16. For example, the final hand region is determined according to whether the hand region detected by the hand region detection unit 16 is equal to or smaller than a predetermined distance, equal to or greater than a predetermined distance, or within a predetermined distance range.

  Next, the operation of the hand region detection device 30 in the second embodiment will be described.

  Since the hand region detection device 20 detects the hand region by the hand region detection unit 16 in the same manner as the hand region detection device 10 in the first embodiment, the description up to that point is omitted. In other words, skin color candidate regions based on human statistical skin color features are selected from the input image, hand candidate regions based on image distribution characteristics in those regions are extracted, and based on the shape complexity of the hand candidate regions To detect the hand area.

  34 to 38 are diagrams illustrating examples of images when skin color candidates are selected from an input image using distance information. As shown in FIG. 34, when an image in which two persons are spreading their hands is input to the image capturing unit 12, the skin color candidate regions I, J, K, and L are selected by the skin color candidate region selection unit 13. (See FIG. 35). Then, the hand region detection unit 16 detects the hand regions in the skin color candidate regions I and K based on the shape complexity as shown in FIG.

  The distance information acquisition unit 32 acquires distance information from the imaging unit 11 to the subject, for example, based on the images captured by the imaging units 11 and 31, and outputs the distance information to the hand region determination unit 33. The hand region determination unit 33 determines the hand region from the hand candidate regions i, j, k, and l extracted by the hand candidate region extraction unit 14 based on the color shading according to the distance information as shown in FIG. . FIG. 38 shows a person hand region located closer to the imaging unit 11 and a person hand region farther than that being removed.

  Thus, by using the distance information, it is possible to detect only the hand of a person behind or at a certain distance.

  In the above-described third embodiment, after the hand region is detected based on the complexity, the final hand region is determined based on the distance information. However, the image capture is performed based on the distance information. A range for detecting the hand region may be determined from the input image from the unit 12, and the hand region may be determined within the range.

It is a block diagram which shows the structure of the hand region detection apparatus in the 1st Embodiment of this invention. It is a figure which shows the example of an input image. It is a figure which shows the example of an image process by RGB information. It is a figure which shows the example of selection of a skin color candidate area | region. It is a figure which shows the example of an image from which the skin color candidate area | region was selected. It is a figure which shows the image processing example by a hue. It is a figure which shows the hue distribution example of a skin color candidate area | region. It is a figure which shows the image processing example by saturation. It is a figure which shows the saturation distribution example of a skin color candidate area | region. It is a figure for demonstrating the detection of the hue area | region of a skin color. It is a figure for demonstrating the detection of the saturation area | region of a skin color. It is a figure which shows the example of a detection of the hue area | region in a hue image. It is a figure which shows the example of a detection of the hue area | region in a saturation image. It is a figure which shows the example of detection of a hand candidate area | region. It is a figure for demonstrating the complexity according to the shape of a hand. It is a figure which shows the example of an input image at the time of closing a hand. It is a figure which shows the calculation result of the shape complexity at the time of closing a hand. It is a figure which shows the example of an input image at the time of opening two fingers. It is a figure which shows the calculation result of the shape complexity at the time of opening two fingers. It is a figure which shows the example of an input image at the time of opening a hand. It is a figure which shows the calculation result of the shape complexity at the time of opening a hand. It is a figure for demonstrating the shape complexity based on the distance from the center of a hand candidate area | region. It is a figure which shows the calculation result of the distance from a center. It is a block diagram which shows the structure of the hand region detection apparatus in the 2nd Embodiment of this invention. It is a figure which shows the example of an image in the time t. It is a figure which shows the example of an image in the time t + 1. It is a figure which shows the example of a detection of a motion area | region. It is a figure which shows the example which detected the hand area | region from the movement area | region. It is a figure which shows the example of detection of a skin color candidate area | region. It is a figure which shows the example of determination of a hand area | region. It is a figure which shows the example of detection of the skin color candidate area | region at the time of not detecting a motion area | region. It is a figure which shows the example of a hand area | region detection result at the time of not detecting a movement area | region. It is a block diagram which shows the structure of the hand area | region detection apparatus in the 3rd Embodiment of this invention. It is a figure which shows the example of an input image. It is a figure which shows the example of detection of a skin color candidate area | region. It is a figure which shows the example of a detection result of a hand area | region. It is a figure which shows the example which estimated the distance of the hand candidate area | region from distance information. It is a figure which shows the detection result of the hand area | region based on distance information.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 hand area detection apparatus, 11 imaging part, 12 image capture part, 13 skin color candidate area selection part, 14 hand candidate area extraction part, 15 shape complexity calculation part, 16 hand area detection part, 20 hand area detection apparatus, 21 Delay circuit unit, 22 motion region detection unit, 23 hand region determination unit, 30 hand region detection device, 31 reference imaging unit, 32 distance information acquisition unit, 33 hand region determination unit

Claims (9)

Hand candidate detection means for detecting a hand candidate region based on a person's statistical skin color feature from an input image;
Shape complexity calculating means for calculating the shape complexity of the hand candidate area extracted by the hand candidate detecting means;
A hand region detecting device comprising: a hand region detecting unit that detects a hand region from the candidate region of the hand based on the shape complexity calculated by the shape complexity calculating unit.   The hand candidate detecting means selects a skin color candidate area from the input image by statistical RGB information, and detects a hand candidate area based on an image distribution characteristic of the skin color candidate area. Hand area detection device.   The hand region detection device according to claim 2, wherein the hand candidate detection means extracts a hand candidate region based on a hue region and a saturation region having the largest number of pixels in the image distribution characteristics of the skin color candidate region. .   2. The hand region detection apparatus according to claim 1, wherein the shape complexity calculating unit calculates the shape complexity based on an area and a peripheral length of the hand candidate region.   2. The hand region detection apparatus according to claim 1, wherein the shape complexity calculation unit calculates the shape complexity based on a distance from a center of the hand candidate region. A motion region detection means for detecting a motion region from an image including the hand region detected by the hand region detection means;
The hand region detection device according to claim 1, further comprising: a hand region determination unit that determines a final hand region from the hand region based on the motion region detected by the motion region detection unit. Distance information acquisition means for acquiring distance information obtained by estimating the position in the three-dimensional space of each pixel of the input image based on images from a plurality of cameras having a predetermined positional relationship;
The hand region detection device according to claim 1, further comprising: a hand region determination unit that determines a final hand region from the hand region based on the distance information acquired by the distance information acquisition unit. A hand candidate detection step of detecting a candidate region of a hand based on a person's statistical skin color feature from an input image;
A shape complexity calculation step of calculating the shape complexity of the candidate region of the hand extracted in the hand candidate detection step;
A hand region detecting step of detecting a hand region from the candidate region of the hand based on the shape complexity calculated in the shape complexity calculating step. In a program that executes processing for detecting a hand area from an input image,
A hand candidate detection step of detecting a candidate region of a hand based on a human skin color feature from the input image;
A shape complexity calculation step of calculating the shape complexity of the candidate region of the hand extracted in the hand candidate detection step;
A hand region detecting step of detecting a hand region from the hand candidate region based on the shape complexity calculated in the shape complexity calculating step.

Images