Object identification method and object identification device

1. An object identification method comprising:

capturing color images and depth images that are aligned;

performing motion detection on the captured color images and the depth images to acquire a motion start frame and a motion end frame;

comparing the motion end frame with the motion start frame in the color images to acquire a set of motion pixels;

performing connected component analysis on the acquired set of motion pixels to obtain at least one connected component;

identifying an object in the at least one connected component;

dividing the at least one connected component into at least one object component based on depth information of the motion end frame in the depth images;

wherein the identifying an object in the at least one connected component comprises identifying the object in the at least one object component,

wherein the performing motion detection on the captured color images and the depth images to acquire the motion start frame and the motion end frame comprises:

determining, for the captured color images, whether a number of pixels whose difference in pixel values between an i-th frame and an (i−1)-th frame is greater than a first threshold exceeds a second threshold, where i is a positive integer greater than 1;

determining, for the captured depth images, whether a number of pixels whose difference in pixel values between an i-th frame and an (i−1)-th frame is greater than a third threshold exceeds a fourth threshold;

determining the i-th frame to be a motion frame in response to at least one of the following conditions, otherwise determining the i-th frame to be a still frame:

(1) the number of pixels whose difference in pixel values between the i-th frame and the (i−1)-th frame of the color images is greater than the first threshold exceeds the second threshold; and

(2) the number of pixels whose difference in pixel values between the i-th frame and the (i−1)-th frame of the depth images is greater than the third threshold exceeds the fourth threshold; and

determining the motion start frame and the motion end frame in the color images and the depth images based on the motion frame and the still frame,

or the performing motion detection on the captured color images and the depth images to acquire the motion start frame and the motion end frame comprises:

determining, for the captured color images, a number X1 of pixels whose difference in pixel values between an i-th frame and an (i−1)-th frame is greater than a first threshold, where i is a positive integer greater than 1;

determining, for the captured depth images, a number X2 of pixels whose difference in pixel values between an i-th frame and an (i−1)-th frame is greater than a third threshold;

determining whether a sum of X1 and X2 is greater than a fifth threshold, and if so, determining the i-th frame to be a motion frame, otherwise determining the i-th frame to be a still frame; and

determining the motion start frame and the motion end frame in the color images and the depth images based on the motion frame and the still frame.

2. The method of claim 1 , wherein the determining the motion start frame and the motion end frame in the color images and the depth images based on the motion frame and the still frame comprises:

determining a previous frame of a detected first motion frame as the motion start frame, and determining a N-th consecutive still frame after the first motion frame as the motion end frame, where N is a preset positive integer.

3. The method of claim 1 , wherein the comparing the motion end frame with the motion start frame in the color images to acquire the set of motion pixels comprises:

performing a pixel-by-pixel comparison of the motion end frame and the motion start frame in the color images to acquire a set of pixels whose difference in pixel values is greater than a sixth threshold; and

defining the set of pixels as the set of motion pixels.

4. The method of claim 1 , wherein the dividing the at least one connected component into at least one object component based on depth information of the motion end frame in the depth images comprises:

determining a pixel having a depth value of zero in the motion end frame in the depth images; and

dividing the at least one connected component into at least one object component along the pixel having the depth value of zero.

5. The method of claim 1 , wherein the identifying the object in the at least one object component comprises identifying the object in the at least one object component based on a depth neural network.

6. The method of claim 1 , wherein the performing connected component analysis on the acquired set of pixels to obtain at least one connected component comprises:

performing connected component analysis on the acquired set of pixels to obtain an initial connected component; and

removing a connected component that does not meet the preset condition for area or aspect ratio, or that is located inside other connected components from the initial connected component to obtain the at least one connected component.

7. The method of claim 2 , wherein the N is dynamically set based on a capture frequency of the color images and the depth images that are aligned.

8. A non-transitory computer readable storage medium having stored thereon computer readable instructions that, when executed, perform the method of claim 1 .

9. An object identification device comprising:

a capturing member, configured to capture color images and depth images that are aligned;

a detecting member, wherein the detecting member is configured to:

determine, for the captured color images, whether a number of pixels whose difference in pixel values between an i-th frame and an (i−1)-th frame is greater than a first threshold exceeds a second threshold, where i is a positive integer greater than 1,

determine, for the captured depth images, whether a number of pixels whose difference in pixel values between an i-th frame and an (i−1)-th frame is greater than a third threshold exceeds a fourth threshold,

determine the i-th frame to be a motion frame in response to at least one of the following conditions, otherwise determine the i-th frame to be a still frame:

(1) the number of pixels whose difference in pixel values between the i-th frame and the (i−1)-th frame of the color images is greater than the first threshold exceeds the second threshold and

(2) the number of pixels whose difference in pixel values between the i-th frame and the (i−1)-th frame of the depth images is greater than the third threshold exceeds the fourth threshold, and

determine a motion start frame and a motion end frame in the color images and the depth images based on the motion frame and the still frame,

or wherein the detecting member is configured to:

determine, for the captured color images, a number X1 of pixels whose difference in pixel values between an i-th frame and an (i−1)-th frame is greater than a first threshold, where i is a positive integer greater than 1;

determine, for the captured depth images, a number X2 of pixels whose difference in pixel values between an i-th frame and an (i−1)-th frame is greater than a third threshold;

determine whether a sum of X1 and X2 is greater than a fifth threshold, and if so, determine the i-th frame to be a motion frame, otherwise determine the i-th frame to be a still frame; and

determine a motion start frame and a motion end frame in the color images and the depth images based on the motion frame and the still frame;

an acquiring member, configured to compare the motion end frame with the motion start frame in the color images to acquire a set of motion pixels;

an analyzing member, configured to perform connected component analysis on the acquired set of motion pixels to obtain at least one connected component;

a dividing member, configured to divide the at least one connected component into at least one object component based on depth information of the motion end frame in the depth images; and

an identifying member, configured to identify an object in the at least one object component.

10. The object identification device of claim 9 , wherein the capturing member comprises an RGBD camera.

11. A shopping cart comprising the object identification device according to claim 9 .