WO2010069167A1 - 识别图像中障碍物的方法和装置 - Google Patents
识别图像中障碍物的方法和装置 Download PDFInfo
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- WO2010069167A1 WO2010069167A1 PCT/CN2009/071577 CN2009071577W WO2010069167A1 WO 2010069167 A1 WO2010069167 A1 WO 2010069167A1 CN 2009071577 W CN2009071577 W CN 2009071577W WO 2010069167 A1 WO2010069167 A1 WO 2010069167A1
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- confidence
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- current frame
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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
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
Definitions
- the present invention relates to the field of obstacle recognition technology, and more particularly to a method and apparatus for identifying obstacles in an image.
- an obstacle generally refers to a three-dimensional object that is higher than the ground.
- the motion compensation based method is one of the commonly used methods for obstacle detection based on monocular vision. Its principle is that the pixel value of the corresponding image point in the image frame of the adjacent time is unchanged at any point of the road plane under the condition that the road is flat and the illumination condition is unchanged in a short time. If it is assumed that all the points in the image at the previous moment are the corresponding imaging points of the points on the road surface, then by the camera motion parameters and the imaging principle, it can be calculated that all the points in the image from the previous moment will be moved after the camera is moved at the next moment.
- the imaginary image composed, the difference between the hypothetical image and the image actually captured at the current time is caused by those points that are not on the road plane.
- the image pixels corresponding to these differences may be obstacles that protrude from the ground.
- the current method is mainly based on the result of motion compensation to judge that there is an obstacle in a certain area of the image. This recognition strategy will result in the result of motion compensation being unsatisfactory under the influence of low accuracy of motion parameters or image noise. The effect is that some non-obstructions are misidentified as obstacles.
- the embodiment of the present invention provides a method and a device for identifying an obstacle in an image, which can reduce the erroneous detection.
- the method for identifying an obstacle in an image provided by the embodiment of the present invention includes:
- An obstacle in the image is determined based on each block area.
- the step of determining whether each block region in the current frame image is an obstacle includes: if the current frame and the same block region corresponding to the previous N frame corresponding to the current frame have a value of the confidence of the obstacle of motion equal to 1 is greater than a first threshold number, the current frame image overall confidence in the block of the 1 _1 ( ⁇ 1 1, otherwise the overall confidence region C_Total block is 0;
- each block region in the current frame image sequentially according to the current frame and the motion obstacle confidence of each block region of the previous N frames closest to the current frame, and the feature obstacle confidence of each block region Whether it is an obstacle; An obstacle in the image is determined based on each block area.
- the characteristic obstacle confidence includes a characteristic obstacle confidence based on a vertical characteristic or a characteristic obstacle confidence based on the texture characteristic.
- the step of determining whether a region of the current frame has a vertical attribute includes:
- aOl calculating the intensity of the vertical direction of a block region / v ;
- the gray value of the row and column pixels is an integer, i' je R , ? is the image block area, N is the image width, and M is the image height.
- step aO1 If the intensity I v of the vertical direction in step aO1 is greater than the intensity threshold, the block area has a vertical attribute, otherwise the block area does not have a vertical attribute.
- the step of determining whether each block region in the current frame image is an obstacle includes: if the current frame and the same block region corresponding to the previous N frame corresponding to the current frame have a value of the confidence of the obstacle of motion equal to The number of 1 is greater than the first number threshold, and the current frame is closest to the current frame The value of the characteristic obstacle confidence of the certain same block region corresponding to the previous N frame is equal to the number of 1 is greater than the second number threshold, and the total confidence of the block region in the current frame image. - ⁇ is 1, otherwise the total confidence C_Total of the block area is 0;
- An image segmentation unit configured to acquire an image of a current frame and a previous N frame that is closest to the current frame, and divide each acquired image in the same manner, and obtain a plurality of divided block regions for each frame image;
- a motion obstacle confidence calculation unit configured to calculate a motion obstacle confidence level of each block region corresponding to a current frame and a previous N frame closest to the current frame
- a first block obstacle recognition unit configured to sequentially determine, according to the current frame and the motion obstacle confidence of each block region of the previous N frames closest to the current frame, whether each block region in the current frame image is sequentially determined As an obstacle;
- An obstacle determining unit configured to determine an obstacle in the image according to each block region.
- the dyskine obstacle confidence calculation unit includes:
- a first motion confidence calculation unit configured to calculate a degree of similarity between the current frame and the imaginary image for a block region, to obtain a first motion confidence C_M_A1;
- a second motion confidence calculation unit configured to calculate a similarity degree between the current frame and the image of the n-k time before the current frame for one block region, to obtain a second motion confidence C_M_A2;
- a dyskine obstacle confidence determining unit configured to: at a value of the first motion confidence C_M_A1 being greater than a first motion threshold, and a ratio of the first motion confidence C_M_A1 to a second motion confidence C_M_A2 being greater than a second motion threshold When it is determined, the motion obstacle confidence C_M of the block region of the current frame is 1, otherwise it is determined that the motion obstacle confidence C_M of the block region of the current frame is 0.
- the first block area obstacle recognition unit includes:
- a first total confidence determining unit configured to: in the current frame and the same block area corresponding to the previous N frame corresponding to the current frame, the value of the motion obstacle confidence value equal to 1 is greater than the first quantity threshold The value, the total confidence C_Total of the block area in the current frame image is determined to be 1, otherwise the total confidence C_Total of the block area is determined to be 0;
- the first identifying unit is configured to determine that the block area is an obstacle when the total confidence C_Total of the certain block area in the current frame image is 1, and otherwise determine that the block area is a non-obstacle.
- An image segmentation unit configured to acquire an image of a current frame and a previous N frame that is closest to the current frame, and divide each acquired image in the same manner, and obtain a plurality of divided block regions for each frame image;
- a motion obstacle confidence calculation unit configured to calculate a motion obstacle confidence level of each block region corresponding to a current frame and a previous N frame closest to the current frame
- a feature obstacle confidence calculation unit configured to calculate a feature obstacle confidence of each block region corresponding to the current frame and the previous N frame closest to the current frame;
- a second block obstacle recognition unit configured to determine a moving obstacle confidence level of each of the block regions according to the current frame and the previous N frames closest to the current frame, and a feature obstacle confidence level of each block region , sequentially determining whether each block area in the current frame image is an obstacle;
- An obstacle determining unit configured to determine an obstacle in the image according to each block region.
- the dyskine obstacle confidence calculation unit includes:
- a first motion confidence calculation unit configured to calculate a degree of similarity between the current frame and the imaginary image for a block region, to obtain a first motion confidence C_M_A1;
- a second motion confidence calculation unit configured to calculate a similarity degree between the current frame and the image of the n-k time before the current frame for one block region, to obtain a second motion confidence C_M_A2;
- a dyskine obstacle confidence determining unit configured to: at a value of the first motion confidence C_M_A1 being greater than a first motion threshold, and a ratio of the first motion confidence C_M_A1 to a second motion confidence C_M_A2 being greater than a second motion threshold When it is determined, the motion obstacle confidence C_M of the block region of the current frame is 1, otherwise it is determined that the motion obstacle confidence C_M of the block region of the current frame is 0.
- the characteristic obstacle confidence includes a feature obstacle confidence based on a vertical characteristic, a characteristic obstacle confidence based on a vertical edge characteristic, or a characteristic obstacle confidence based on a texture characteristic Degree.
- the characteristic obstacle confidence calculation unit includes:
- a vertical attribute determining unit configured to determine whether a block area of the current frame has a vertical attribute, and notify the feature obstacle confidence determining unit of the determination result;
- the feature obstacle confidence determining unit is configured to determine that the feature obstacle of the current frame has a confidence level of 1 when the current frame has a vertical attribute, and otherwise determine the feature obstacle confidence of the block area of the current frame.
- C_F is 0.
- the second block obstacle recognition unit includes:
- a second total confidence determining unit configured to: in the current frame and the same block area corresponding to the previous N frame, the value of the motion obstacle confidence value equal to 1 is greater than the first quantity threshold, and Determining the total of the block area in the current frame image when the value of the feature obstacle confidence of the same frame region corresponding to the previous N frame corresponding to the current frame is equal to 1 is greater than the second number threshold Confidence C_Total is 1, otherwise the total confidence of the block area is determined (_1 1 ( ⁇ 1 is 0; the second identification unit is used to know the total confidence of the certain block area in the current frame image)
- C_Total it is determined that the block area is an obstacle, otherwise it is determined that the block area is a non-obstacle.
- a method and a device for identifying an obstacle in an image according to an embodiment of the present invention are compared with a conventional motion compensation method based on the absolute degree of motion compensation, and a relative motion compensation based The strategy reduces the false detection rate and accurately determines the obstacles in the image.
- Another method and apparatus for identifying an obstacle in an image is not only based on a motion-based absolute degree strategy but also a motion compensation-based relative method, compared with a conventional motion compensation-only method. Based on this strategy, the strategy of feature analysis is further integrated, which further reduces the false detection rate and accurately determines the obstacles in the image.
- FIG. 1 is a flow chart of a method of identifying obstacles in an image, in accordance with an embodiment of the present invention
- FIG. 2 is a flow chart of a method for calculating a motion obstacle confidence level for each block region of a current frame, in accordance with an embodiment of the present invention
- FIG. 3 is a schematic diagram showing the relationship between a set world coordinate system and a world coordinate system of a camera of a previous time image according to an embodiment of the present invention
- FIG. 4 is a schematic diagram of a virtual image of a current moment according to an embodiment of the present invention.
- FIG. 5 is a structural view of a device for identifying an obstacle in an image according to an embodiment of the present invention
- FIG. 6 is a structural view of another device for identifying an obstacle in an image according to an embodiment of the present invention.
- a method for identifying an obstacle in an image includes: acquiring an image of a current frame and a previous N frame that is closest to the current frame, and dividing each acquired image in the same manner, for each frame image. Obtaining a plurality of divided block regions; calculating a motion obstacle confidence of each block region corresponding to the current frame and the previous N frames closest to the current frame; according to the current frame and the previous N frames closest to the current frame. The motion obstacle confidence level of each of the block regions sequentially determines whether each block region in the current frame image is an obstacle; and determines an obstacle in the image according to each block region.
- the invention compared with the traditional motion compensation only method, not only the strategy based on the absolute degree of motion compensation but also the relative strategy based on motion compensation is adopted, thereby reducing the false detection rate and accurately determining the image. Obstacle in the middle.
- Another method for identifying an obstacle in an image includes: acquiring an image of a current frame and a previous N frame that is closest to the current frame, and dividing each acquired image in the same manner for each frame. Obtaining a plurality of divided block regions in the image; calculating a motion obstacle confidence level of each block region corresponding to the current frame and the previous N frames closest to the current frame, and a characteristic obstacle confidence level of each block region; The current frame and each of the previous N frames closest to the current frame The trajectory confidence of the block area, and the characteristic obstacle confidence of each block area, sequentially determines whether each block area in the current frame image is an obstacle; and determines an obstacle in the image according to each block area.
- FIG. 1 there is shown a flow chart of a method of identifying obstacles in an image in accordance with an embodiment of the present invention.
- the strategy based on the absolute degree of motion compensation is adopted, and the relative strategy based on motion compensation is also adopted.
- the strategy of feature analysis is further integrated. Specifically include:
- Step 101 Divide an image block area, specifically, obtain an image of a current frame and a previous N frame that is closest to the current frame, and divide each acquired image in the same manner, and obtain a plurality of divisions for each frame image. Block area.
- an image of the current frame ie, an image representing the time n
- each of the above-mentioned images is divided into a plurality of block regions, and the shape of the block region may be a rectangle, or may be any shape that can completely divide the image, such as a triangle, which is divided into: ⁇ ⁇ ⁇ pixels. Rectangular block areas that do not overlap each other, such that the same number of divided rectangular block areas are obtained for each frame of image.
- the specific segmentation method is not limited herein, as long as it can be divided into a plurality of rectangular block regions.
- Step 102 Calculate the motion obstacle confidence of each block region corresponding to the current frame and the previous frame closest to the current frame.
- the so-called skeptical confidence refers to whether the block area after the motion compensation is an obstacle. Confidence, the motion compensation here is slightly different from the existing motion compensation.
- the existing motion compensation usually only includes the absolute degree based on motion compensation, and the motion compensation in the present invention includes the absolute degree and relative degree based on the motion compensation.
- Step 201 Acquire camera motion parameters.
- the camera motion parameters obtained in the embodiments of the present invention may be obtained by a sensor, such as a speed sensor and a gyro sensor, or may be obtained by a sequence image, such as an optical flow method and a motion recovery structure (Structure). From Motion, SFM) method based on feature check, direct method, etc.
- SFM motion recovery structure
- the method estimates the motion parameters, 2 "Transforming camera geometry to a virtual downward-looking camera: robust ego-motion estimation and ground-layer detection” using an improved direct method to obtain the motion parameters.
- the embodiment of the present invention uses the direct estimation method of Document 1 to acquire camera motion parameters.
- the world coordinate system [ ⁇ ; , H] and the origin coordinate G of the camera coordinate system [ ⁇ ' ; ⁇ , , ⁇ ] of the image of the previous moment are set, and the rotation angle may be set between the coordinate axes.
- the axis of the world coordinate system is parallel to the road plane and perpendicular to the road plane. As shown in Figure 3.
- a pixel point c) in the image of the previous moment is r
- c is the row coordinate and column coordinate of the point in the image respectively
- P is the point of the point P 0 (X w , ⁇ , Z w ) on the road plane.
- the point p can be calculated from the camera imaging formula (also known as the camera imaging formula). World sitting
- ⁇ is the rotation angle of the camera coordinate system around the world coordinate system U, Z axis, For camera external parameters, obtained when the camera is installed;
- the coordinates of the camera coordinate system origin in the world coordinate system after movement are 7 ⁇ ⁇ ' ⁇ + ⁇ , and the rotation angle of the camera coordinate system around the world coordinate system ⁇ becomes ⁇ . , ⁇ + ⁇ , ⁇ .
- the rotation matrix and translation matrix of the camera at this time and the calculated ⁇ .
- the right end of the equation is known, and the left end unknown number, that is, the point p can be obtained.
- the imaging coordinates in the imaginary image Specifically, based on the above transformation, all the points on the previous image F ⁇ can be moved according to the motion parameters, and a new hypothetical image F demo is generated.
- the plane area in the image is exactly the same as the plane area of the image F Dock at the current time, but the plane area is different.
- the following principle is used to determine the confidence that the area is a three-dimensional object.
- Step 203 Calculate, for a block region, a similarity degree between the current frame and the virtual image of the previous n-k time, and use the obtained similarity degree as the first motion confidence C_M_A1 based on the motion compensation.
- the degree of similarity between the current frame and the imaginary image of the previous nk time is referred to as the first motion confidence C_M_A1, and the first motion confidence is actually based on the absolute degree of motion compensation;
- the degree of similarity of the nk time image is called the second motion confidence C_M_A2, and the second motion confidence is actually not motion compensated; and the ratio of the first motion confidence C_M_A1 to the second motion confidence C_M_A2 is actually based on motion compensation. Relative degree.
- the step 203 includes: searching for pixels with the same coordinates in the imaginary image for all the pixels in a certain block region in the image, and then calculating the current time image of the block region and the imaginary image of the current time based on the pixel values of the corresponding points.
- the size of the block area is N*M, which is the gray value of the pixel ( ) in the image
- X ' ( , j) is the gray value of the pixel ( , j) in the imaginary image F n of the image.
- Step 204 Calculate, for a block region, a similarity degree between the current frame and the previous nk time image, and use the obtained similarity degree as the second motion confidence C_M_A2 that does not perform motion compensation; specifically, for a block region in the image. All the pixels, find the pixels with the same coordinates in F ⁇ , and then calculate the similarity degree between the current time image of the block region and the previous nk time image based on the pixel values of the corresponding point pair, thereby obtaining the block region as the second obstacle
- the motion confidence C_M_A2 wherein the similarity NC can be calculated using a normalized correlation value (NC):
- N*M which is the gray value of the pixel ( ) in the image.
- X "(i, j is the pixel (i, j) gray value in the image F: at the previous n-k time.
- Step 205 Determine a motion obstacle confidence C_M.
- the block of the current frame The occlusion obstacle confidence C_M of the region is 1, otherwise the kinetic obstacle confidence C_M of the block region of the current frame is 0.
- the above-mentioned dyskine obstacle confidence C_M is based on the absolute degree of motion compensation (ie, the case where the value of C_M_A1 is greater than the first motion threshold), and the relative degree based on the motion compensation (ie, the ratio of C_M_A1 and C_M_A2 is greater than the second motion threshold) Situation) obtained.
- the ratio of the first motion confidence C_M_A1 to the second motion confidence C_M_A2 is a generalized concept, and the ratio may be the value of C_M_A1/C_M_A2 or (C_M_A1-C_M_A2)/(1- The value of C_M_A2) can also be the value of other representations.
- step 206 the above steps 203 ⁇ 205 are repeatedly performed until the motion obstacle confidence of each block region is calculated.
- Step 103 Calculate a feature obstacle confidence C_F of each block region corresponding to the current frame and the previous N frames closest to the current frame.
- the feature obstacle confidence includes a feature obstacle confidence based on a vertical characteristic, a feature obstacle confidence based on a vertical edge characteristic, or a feature obstacle confidence based on a texture characteristic.
- the vertical characteristic is taken as an example to illustrate how to calculate the characteristic obstacle confidence C_F.
- Step a determining whether a region of the current frame has a vertical attribute, if yes, the feature obstacle confidence C_F of the block region of the current frame is 1, otherwise the feature obstacle confidence C_F of the block region of the current frame is 0.
- the specific step of determining whether a region of the current frame has a vertical attribute includes:
- aOl calculating the intensity of the vertical direction of a block region / v ;
- the gray value of the column of pixels, k is an integer, i' je R , is the image block area, N is the image block area width, and M is the image block area height.
- step b step a is repeated until the characteristic obstacle confidence of each block region is calculated.
- Step 104 sequentially determine, according to the current frame and the motion obstacle confidence of each block region of the previous N frames closest to the current frame, and the feature obstacle confidence of each block region, sequentially determine each of the current frame images. Whether the block area is an obstacle. Specifically, the confidence level of the motion obstacle corresponding to the latest N frame is taken as a region, and is set to C_M t , C_M t _ 1 , . . . , C_M t _ N _ 1 , and the value of the statistical obstacle of the motion obstacle is equal to 1.
- Sum_M taking the block region corresponding to the feature confidence of the last N frames, set to C_F t , C_F M ,..., CV N 4 , the statistical feature obstacle confidence value is equal to the number of Sum_F; if Sum_M is greater than the first The quantity threshold S_M_Limit, and Sum_F is greater than the second number threshold C_F_Limit, then the total confidence C_Total is 1, otherwise 0. If the total confidence of a certain block area in the current frame image ( _1 1 ( ⁇ 1 is 1 , the block area is Obstacle, otherwise the block area is non-obstacle.
- the total confidence C_Total of the block area in the current frame image is 1, otherwise The total confidence C_Total of the block area is 0; the total confidence of a block area in the current frame image.
- Step 105 Determine an obstacle in the image according to each block area.
- FIG. 1 shows a preferred embodiment. In practical applications, there may be another case where only motion compensation (including absolute degree and relative degree) is performed, and compensation is no longer performed based on feature analysis. The same can be achieved to reduce the false detection rate. In this embodiment, the steps are substantially the same as those shown in FIG.
- step 103 is no longer performed, and the corresponding step 104 becomes: according to the current frame and each of the previous N frames closest to the current frame.
- the occlusion obstacle confidence of the block area sequentially determines whether each block area in the current frame image is an obstacle.
- the step of determining whether each block region in the current frame image is an obstacle includes: specifically, taking a region to correspond to the confidence of the motion obstacle of the latest N frame, and setting C_M t , C_M M , ..., C_M t _ N4 , the value of the statistical obstacle of the dynamometer is equal to the number of Sum_M; Sum_M is greater than the first number threshold S_M_Limit, then the total confidence C_Total is 1, otherwise 0.
- the embodiment of the invention further provides a device for identifying an obstacle in an image, see FIG. 5, and a figure thereof.
- an image segmentation unit 501 there are: an image segmentation unit 501, a motion obstacle confidence calculation unit 502, a feature obstacle confidence calculation unit 503, a second block obstacle recognition unit 504, and an obstacle determination unit 505. among them,
- the image segmentation unit 501 is configured to acquire an image of a current frame and a previous N frame that is closest to the current frame, divide the acquired image of each frame in the same manner, and obtain a plurality of divided block regions for each frame of the image;
- the dyskine obstacle confidence calculation unit 502 is configured to calculate a motion obstacle confidence level of each block region corresponding to the current frame and the previous N frames closest to the current frame,
- the feature obstacle confidence calculation unit 503 is configured to calculate a feature obstacle confidence level of each block region corresponding to the current frame and the previous N frames closest to the current frame; the feature obstacle confidence may include a feature based on vertical characteristics Obstacle confidence, characteristic obstacle confidence based on vertical edge characteristics, or characteristic obstacle confidence based on texture characteristics.
- a second block obstacle recognition unit 504 configured to perform a motion obstacle confidence level of each of the block regions according to the current frame and the previous N frames closest to the current frame, and a characteristic obstacle of each block region Degree, sequentially determining whether each block area in the current frame image is an obstacle;
- An obstacle determining unit 505 is configured to determine an obstacle in the image according to each block region.
- the kinetic obstacle confidence calculation unit 502 may further include: a hypothetical image generation unit, a first motion confidence calculation unit, a second motion confidence calculation unit, and a motion obstacle confidence determination Unit. among them,
- a first motion confidence calculation unit configured to calculate a degree of similarity between the current frame and the imaginary image for a block region, to obtain a first motion confidence C_M_A1;
- a second motion confidence calculation unit configured to calculate a similarity degree between the current frame and the image of the n-k time before the current frame for one block region, to obtain a second motion confidence C_M_A2;
- a dyskine obstacle confidence determining unit configured to: at a value of the first motion confidence C_M_A1 being greater than a first motion threshold, and a ratio of the first motion confidence C_M_A1 to a second motion confidence C_M_A2 being greater than a second motion threshold Determining, the motion obstacle confidence C_M of the block area of the current frame is 1, otherwise determining that the motion obstacle confidence C_M of the block area of the current frame is 0; when the above characteristic obstacle confidence is based on the vertical characteristic,
- the feature obstacle confidence calculation unit 503 may include: a vertical attribute determination unit and a feature obstacle confidence determination unit.
- the vertical attribute determining unit is configured to determine whether a certain block area of the current frame has a vertical attribute, and notify the feature obstacle confidence determining unit of the determination result;
- the feature obstacle confidence determining unit is configured to determine that the feature obstacle of the current frame has a confidence level of 1 when the current frame has a vertical attribute, and otherwise determine the feature obstacle confidence of the block area of the current frame.
- C_F is 0.
- the second block obstacle recognition unit 504 may include: a second total confidence determining unit and a second identifying unit, where
- a second total confidence determining unit configured to: in the current frame and the same block area corresponding to the previous N frame, the value of the motion obstacle confidence value equal to 1 is greater than the first quantity threshold, and Determining the total of the block area in the current frame image when the value of the feature obstacle confidence of the same frame region corresponding to the previous N frame corresponding to the current frame is equal to 1 is greater than the second number threshold
- the confidence C_Total is 1, otherwise the total confidence of the block area is determined ( _1 1 ( ⁇ 1 is 0; the second identifying unit is configured to know that the total confidence C_Total of the certain block area in the current frame image is 1) Determining that the block area is an obstacle, otherwise determining that the block area is a non-obstacle.
- Embodiments of the present invention also provide a device for identifying an obstacle in an image, see FIG. 6, and
- the difference between the embodiment shown in FIG. 5 is that the feature obstacle confidence calculation unit 503 is not included, that is, the apparatus shown in FIG. 6 includes: an image segmentation unit 601, a motion obstacle confidence calculation unit 602, and a first region obstacle.
- the image segmentation unit 601 is configured to acquire an image of a current frame and a previous N frame that is closest to the current frame, and divide each acquired image in the same manner, and obtain a plurality of divided block regions for each frame image;
- the dyskine obstacle confidence calculation unit 602 is configured to calculate a motion obstacle confidence level of each block region corresponding to the current frame and the previous N frames closest to the current frame,
- the first block obstacle recognition unit 603 is configured to sequentially determine each block region in the current frame image according to the current frame and the motion obstacle confidence of each block region of the previous N frames closest to the current frame. Whether it is an obstacle;
- the obstacle determining unit 604 is configured to determine an obstacle in the image according to each block region.
- the kinetic obstacle confidence calculation unit 602 is the same as the kinic obstacle confidence calculation unit 502 in FIG. 5, and includes: a hypothetical image generation unit, a first motion confidence calculation unit, a second motion confidence calculation unit, and a motion obstacle The reliability determining unit is not described in detail.
- the first block area obstacle recognition unit 603 may include:
- a first total confidence determining unit configured to determine, when the value of the motion obstacle confidence value of the same block region corresponding to the current frame and the previous N frame corresponding to the current frame is equal to 1 and greater than the first quantity threshold The total confidence C_Total of the block area in the current frame image is 1, otherwise it is determined that the total confidence C_Total of the block area is 0;
- the first identifying unit is configured to determine that the block area is an obstacle when the total confidence C_Total of the certain block area in the current frame image is 1, and determine that the block area is a non-obstacle.
- the apparatus for identifying obstacles in an image provided by the present invention not only adopts a strategy based on the absolute degree of motion compensation but also adopts a relative strategy based on motion compensation, compared with a conventional logic device based only on motion compensation, and On this basis, the strategy of feature analysis is further integrated, which improves the accuracy of detection while reducing the false detection rate, and can accurately determine the obstacles in the image.
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JP2011539877A JP5276721B2 (ja) | 2008-12-15 | 2009-04-30 | 画像中の障害物を識別する方法および装置 |
US13/133,546 US8406474B2 (en) | 2008-12-15 | 2009-04-30 | Method and apparatus for identifying obstacle in image |
DE112009003144.7T DE112009003144B4 (de) | 2008-12-15 | 2009-04-30 | Verfahren und Vorrichtung zum Feststellen eines Hindernisses in einem Bild |
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CN101419667B (zh) | 2010-12-08 |
JP2012512446A (ja) | 2012-05-31 |
DE112009003144B4 (de) | 2019-05-29 |
CN101419667A (zh) | 2009-04-29 |
DE112009003144T5 (de) | 2012-07-05 |
JP5276721B2 (ja) | 2013-08-28 |
US20110262009A1 (en) | 2011-10-27 |
US8406474B2 (en) | 2013-03-26 |
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