Target recognition method and appartus, storage medium, and electronic device

Abstract

A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

What is claimed is: 
     
         1 . A method for identifying a target, comprising:
 acquiring a first image and a second image, the first image and the second image each comprising a target to be determined;   generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to targets to be determined in the first image and the second image; and   performing validity determination on the prediction path, and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined,   wherein before generating the prediction path based on the first image and the second image, the method further comprises:   determining a preliminary sameness probability value of the targets to be determined respectively contained in the first image and the second image according to temporal information, spatial information, and image feature information of the first image and temporal information, spatial information, and image feature information of the second image;   wherein generating the prediction path based on the first image and the second image comprises:   generating the prediction path based on the first image and the second image if the preliminary sameness probability value is greater than a preset value.   
     
     
         2 . The method according to  claim 1 , wherein determining the preliminary sameness probability value of the targets to be determined respectively contained in the first image and the second image according to the temporal information, the spatial information, and the image feature information of the first image and the temporal information, the spatial information, and the image feature information of the second image comprises:
 inputting the first image, the second image, and a difference in temporal information and a difference in spatial information between the first image and the second image into a Siamese Convolutional Neural Network (Siamese-CNN) to obtain the preliminary sameness probability value of the targets to be determined in the first image and the second image.   
     
     
         3 . The method according to  claim 1 , wherein performing the validity determination on the prediction path and determining, according to the determination result, whether the targets to be determined in the first image and the second image are the same target to be determined comprises:
 performing, through a neural network, validity determination on the prediction path and determining, according to the determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.   
     
     
         4 . The method according to  claim 1 , wherein performing, through the neural network, the validity determination on the prediction path and determining whether the targets to be determined in the first image and the second image are the same target to be determined according to the determination result comprises:
 acquiring a temporal difference between adjacent images in the prediction path according to temporal information of the adjacent images; acquiring a spatial difference between the adjacent images according to spatial information of the adjacent images; and acquiring a feature difference between the targets to be determined in the adjacent images according to feature information of the targets to be determined in the adjacent images;   inputting the obtained temporal difference, spatial difference, and feature difference between the adjacent images in the prediction path into a Long Short-Term Memory (LSTM) network to obtain an identification probability of the targets to be determined in the prediction path; and   determining, according to the identification probability of the targets to be determined in the prediction path, whether the targets to be determined in the first image and the second image are the same target to be determined.   
     
     
         5 . The method according to  claim 4 , wherein acquiring the feature difference between the targets to be determined in the adjacent images according to the feature information of the targets to be determined in the adjacent images comprises:
 separately acquiring feature information of the targets to be determined in the adjacent images through the Siamese-CNN; and   acquiring the feature difference between the targets to be determined in the adjacent images according to the separately acquired feature information.   
     
     
         6 . An apparatus for identifying a target, comprising:
 a processor; and   a memory for storing instructions executable by the processor;   wherein the processor is configured to:   acquire a first image and a second image, the first image and the second image each comprising a target to be determined;   generate a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to targets to be determined in the first image and the second image; and   perform validity determination on the prediction path and determine, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined,   wherein the processor is further configured to:   before generating the prediction path based on the first image and the second image, determine, according to temporal information, spatial information, and image feature information of the first image and temporal information, spatial information, and image feature information of the second image, a preliminary sameness probability value of the targets to be determined respectively contained in the first image and the second image; and   wherein the operation of generating the prediction path based on the first image and the second image comprises:   generating the prediction path based on the first image and the second image if the preliminary sameness probability value is greater than a preset value.   
     
     
         7 . The apparatus according to  claim 6 , wherein the processor is specifically configured to:
 input the first image, the second image, and a difference in temporal information and a difference in spatial information between the first image and the second image into a Siamese Convolutional Neural Network (Siamese-CNN) to obtain a preliminary sameness probability value of the targets to be determined in the first image and the second image.   
     
     
         8 . The apparatus according to  claim 7 , wherein the processor is specifically configured to:
 perform validity determination on the prediction path through a neural network and determine, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.   
     
     
         9 . The apparatus according to  claim 8 , wherein the operation of performing the validity determination on the prediction path through the neural network and determine, according to the determination result, whether the targets to be determined in the first image and the second image are the same target to be determined comprises:
 acquiring a temporal difference between adjacent images in the prediction path according to temporal information of the adjacent images; acquire a spatial difference between the adjacent images according to spatial information of the adjacent images; and acquire a feature difference between the targets to be determined in the adjacent images according to feature information of the targets to be determined in the adjacent images;   inputting the obtained temporal difference, spatial difference, and feature difference between the adjacent images in the prediction path into a Long Short-Term Memory (LSTM) network to obtain an identification probability of the targets to be determined in the prediction path; and   determining, according to the identification probability of the targets to be determined in the prediction path, whether the targets to be determined in the first image and the second image are the same target to be determined.   
     
     
         10 . The apparatus according to  claim 9 , wherein the operation of acquiring the feature difference between the targets to be determined in the adjacent images according to the feature information of the targets to be determined in the adjacent images comprises:
 separately acquiring feature information of the targets to be determined in the adjacent images through the Siamese-CNN; and   acquiring the feature difference between the targets to be determined in the adjacent images according to the separately acquired feature information.   
     
     
         11 . A non-transitory computer-readable storage medium, having computer program instructions stored thereon, wherein the program instructions, when being executed by a processor, are configured to perform the operations of:
 acquiring a first image and a second image, the first image and the second image each comprising a target to be determined;   generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to targets to be determined in the first image and the second image; and   performing validity determination on the prediction path, and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined,   wherein before generating the prediction path based on the first image and the second image, the program instructions being executed by the processor, are further configured to perform the operation of:   determining a preliminary sameness probability value of the targets to be determined respectively contained in the first image and the second image according to temporal information, spatial information, and image feature information of the first image and temporal information, spatial information, and image feature information of the second image;   wherein the operation of generating the prediction path based on the first image and the second image comprises:   generating the prediction path based on the first image and the second image if the preliminary sameness probability value is greater than a preset value.   
     
     
         12 . The non-transitory computer-readable storage medium according to  claim 11 , wherein the operation of determining the preliminary sameness probability value of the targets to be determined respectively contained in the first image and the second image according to the temporal information, the spatial information, and the image feature information of the first image and the temporal information, the spatial information, and the image feature information of the second image comprises:
 inputting the first image, the second image, and a difference in temporal information and a difference in spatial information between the first image and the second image into a Siamese Convolutional Neural Network (Siamese-CNN) to obtain the preliminary sameness probability value of the targets to be determined in the first image and the second image.   
     
     
         13 . The non-transitory computer-readable storage medium according to  claim 11 , wherein the operation of performing the validity determination on the prediction path and determining, according to the determination result, whether the targets to be determined in the first image and the second image are the same target to be determined comprises:
 performing, through a neural network, validity determination on the prediction path and determining, according to the determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.   
     
     
         14 . The non-transitory computer-readable storage medium according to  claim 11 , wherein the operation of performing, through the neural network, the validity determination on the prediction path and determining whether the targets to be determined in the first image and the second image are the same target to be determined according to the determination result comprises:
 acquiring a temporal difference between adjacent images in the prediction path according to temporal information of the adjacent images; acquiring a spatial difference between the adjacent images according to spatial information of the adjacent images; and acquiring a feature difference between the targets to be determined in the adjacent images according to feature information of the targets to be determined in the adjacent images;   inputting the obtained temporal difference, spatial difference, and feature difference between the adjacent images in the prediction path into a Long Short-Term Memory (LSTM) network to obtain an identification probability of the targets to be determined in the prediction path; and   determining, according to the identification probability of the targets to be determined in the prediction path, whether the targets to be determined in the first image and the second image are the same target to be determined.   
     
     
         15 . The non-transitory computer-readable storage medium according to  claim 14 , wherein the operation of acquiring the feature difference between the targets to be determined in the adjacent images according to the feature information of the targets to be determined in the adjacent images comprises:
 separately acquiring feature information of the targets to be determined in the adjacent images through the Siamese-CNN; and   acquiring the feature difference between the targets to be determined in the adjacent images according to the separately acquired feature information.