WO2018107488A1 - 基于提升直觉模糊树的目标跟踪方法及装置 - Google Patents
基于提升直觉模糊树的目标跟踪方法及装置 Download PDFInfo
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- the present invention relates to the field of target tracking, and in particular to a target tracking method and apparatus based on an improved intuitionistic fuzzy tree.
- Online target tracking is a hot research topic in computer vision. It is of great significance for high-level visual research such as motion recognition, behavior analysis and scene understanding, and has wide applications in video surveillance, intelligent robots, human-computer interaction and other fields. prospect.
- the missed detection target cannot find the detected observation object associated with it, and it is impossible to find valid information for the track update of these missed detection targets through the data association, and the track accuracy is lowered.
- the present invention proposes a target tracking method based on lifting an intuitionistic fuzzy tree.
- the target tracking method based on the lifting intuitionistic fuzzy tree comprises: performing motion detection on the current video frame, detecting the possible moving object as an observation result; and correlating the observation result with the prediction result of the target, wherein the prediction result is at least using the previous video.
- the target includes a reliable target and a temporary target; and trajectory management is performed on the unrelated observation and prediction results, including online tracking acquisition candidates for the prediction results of the unassociated reliable target
- the candidate inductive fuzzy tree is used to match the candidate results by using the improved intuitionistic fuzzy tree; the correlation result and the matching result are used to obtain the trajectory of the target of the current frame, including the candidate for the successful target with successful matching
- the prediction result is filtered and updated to acquire the trajectory; the trajectory of the target of the current frame is used for prediction, and the intuitionistic fuzzy tree is updated for the reliable target update with successful association or matching success.
- the target tracking device based on the lifting intuitionistic fuzzy tree comprises: a processor and a camera, the processor is connected to the camera; the processor is configured to perform motion detection on the current video frame acquired from the camera, and detect the possible moving object as an observation result; And the target's prediction results And the prediction result is obtained by predicting at least the trajectory of the target of the previous video frame, the target includes a reliable target and a temporary target; and the trajectory management is performed on the unrelated observation and the predicted result, including the pair being unrelated
- the prediction result of the reliable target is obtained by online tracking to obtain the candidate result, and the candidate inductive result is matched by the improved intuitionistic fuzzy tree of the unassociated reliable target; the trajectory of the target of the current frame is obtained by using the correlation result and the matching result, including successful matching
- the reliable target uses its matching successful candidate result to filter and update its prediction result to obtain the trajectory; use the trajectory of the target of the current frame to perform prediction,
- the beneficial effects of the present invention are: obtaining candidate results by performing online tracking on the prediction results of unassociated reliable targets, and matching the candidate results with the improved intuitionistic fuzzy trees of the unassociated reliable targets, and if the matching is successful, using the matching
- the successful candidate results filter and update the prediction result of the reliable target to obtain its trajectory, so that in the event that the missed detection occurs and the target cannot find the associated observation object, the improved intuitionistic fuzzy tree can be used to find a matching and applicable
- the trajectory filters the updated candidate results, thereby improving the accuracy of the target trajectory and improving the performance of the target tracking.
- FIG. 1 is a flow chart of a first embodiment of a target tracking method based on lifting an intuitionistic fuzzy tree according to the present invention
- FIG. 2 is a schematic diagram of a hard decision function and a fuzzy decision function of a branch node in an example of a second embodiment of the object tracking method based on the improved intuitionistic fuzzy tree;
- FIG. 3 is a schematic diagram of a fuzzy decision function and an intuitionistic fuzzy decision function of a branch node in an example of a second embodiment of the object tracking method based on the improved intuitionistic fuzzy tree;
- FIG. 4 is a flow chart of a third embodiment of a target tracking method based on lifting an intuitionistic fuzzy tree according to the present invention.
- FIG. 5 is a flow chart of a fourth embodiment of a target tracking method based on lifting an intuitionistic fuzzy tree according to the present invention.
- FIG. 6 is a flowchart of a feature selection criterion training in a fourth embodiment of a target tracking method based on an improved intuitionistic fuzzy tree according to the present invention
- FIG. 7 is a flow chart of a fifth embodiment of a target tracking method based on lifting an intuitionistic fuzzy tree according to the present invention.
- FIG. 8 is a flow chart of a sixth embodiment of a target tracking method based on lifting an intuitionistic fuzzy tree according to the present invention.
- FIG. 9 is a flowchart of a seventh embodiment of a target tracking method based on lifting an intuitionistic fuzzy tree according to the present invention.
- FIG. 10 is a schematic structural diagram of a first embodiment of a target tracking apparatus based on an improved intuitionistic fuzzy tree according to the present invention.
- the first embodiment of the present invention is based on an object tracking method for improving an intuitionistic fuzzy tree.
- the first embodiment includes:
- the motion detection algorithm such as frame difference method, optical flow method and background subtraction method is used to detect the motion of the current video frame, so as to find out the pixels belonging to the foreground of the motion, supplemented by median filtering and simple morphological processing, and finally obtain the current video.
- Possible moving objects in the frame are used as observation objects.
- An observation object is an image block in the current video frame. Generally, the shape of the observation object is a rectangle.
- Targets include reliable targets for stable tracking and temporary targets for unstable tracking.
- the target state in this step that is, whether each target is marked as a reliable target or a temporary target, is determined by the trajectory management of the previous video frame.
- the temporary target includes a new target established by the observation that the previous video frame is a candidate result that is not associated and is not a successful match, and a target whose consecutively associated successful number of frames is less than or equal to the first frame number threshold and has not been deleted.
- a reliable target includes a target whose number of consecutively successful frames is greater than the first frame number threshold and has not been deleted.
- the prediction result of the target is obtained by predicting at least the trajectory of the target of the previous video frame.
- S3 Perform trajectory management on uncorrelated observations and prediction results, including performing on-line tracking of candidate results for unassociated reliable targets, and using improved intuitionistic fuzzy tree pairs of unassociated reliable targets Candidate results are matched.
- a plurality of image blocks are selected as candidate results in the prediction result position of the reliable target and the surrounding range, and the size of the image block is generally consistent with the size of the prediction result, and the size of the specified range and the number of candidate results are generally experienced.
- the value is determined.
- Candidate results can include unrelated observations within a specified range. Adjacent candidate results may not overlap each other or may partially overlap.
- a lifting intuitionistic fuzzy tree of unreliable reliable targets is used as a classifier, and the classification result of the classifier has two types: a reliable target and an unreliable target.
- the candidate result is calculated as the intuitionistic fuzzy membership of the test sample belonging to the reliable target category.
- the matching is successful, 0.5 ⁇ 1 ⁇ 1 and the first threshold value ⁇ 1 is greater than the sixth threshold, 0.5 ⁇ ⁇ 2 ⁇ 1.
- the candidate target with the largest intuitionistic fuzzy membership degree may also be selected from the candidate results, and then Determining whether the intuitionistic fuzzy membership degree of the selected candidate result is greater than the first threshold value ⁇ 1 , and whether the appearance feature similarity measure of the prediction result is greater than the second threshold value ⁇ 2 , and if the foregoing two conditions are met, the matching is successful.
- the target status is updated based on the association result and the matching result, including the establishment, deletion, and state modification of the target.
- the method includes: establishing a new temporary target for the observation result of the candidate result that is not associated and not successfully matched; changing the temporary target whose continuous association is greater than the first frame number threshold ⁇ 1 into a reliable target; deleting the continuous association
- the number of successful frames is greater than the temporary target of the second frame number threshold ⁇ 2 ; the number of frames in which the consecutive association is unsuccessful is greater than the third frame threshold ⁇ 3 , and the matching result is a reliable target of matching failure, and the matching result is that the matching failure means
- the candidate inference result is calculated by using the ascending intuitionistic fuzzy tree as the test sample, and the intuitionistic fuzzy membership degree belonging to the reliable target class is less than or equal to the sixth threshold value ⁇ 6 and satisfies 0 ⁇ 6 ⁇ 1 .
- ⁇ 1 is a positive integer greater than 1
- ⁇ 2 and ⁇ 3 are both positive integers and satisfy
- S4 Acquire the trajectory of the target of the current frame by using the association result and the matching result, perform prediction by using the trajectory of the target of the current frame, and update the intuitionistic fuzzy tree for the reliable target update with successful association or matching success.
- the reliable target matching success is filtered and updated by the candidate result of the matching success to obtain the trajectory.
- the associated successful target uses its associated observations to filter and update its prediction results to obtain the trajectory.
- the new temporary target will be the corresponding observation result as the trajectory, and the temporary target and the association that are not successful and not deleted will not be associated.
- a reliable target that succeeds and matches unsuccessfully and has not been deleted has its predicted result as a trajectory.
- the trajectory of the target of the current frame is used for prediction, and the obtained result can be used as the target prediction result for the target tracking of the next frame.
- the Kalman filter is used to predict the trajectory of the target of the current frame to obtain the prediction result of the target of the next frame, and the Kalman filter can also be used for the prediction result and the corresponding observation result/ The candidate results are filtered to obtain the trajectory of the target.
- the target image block corresponding to the reliable target with successful association or matching success is updated to enhance the intuitionistic fuzzy tree.
- the target image block may not include the trajectory information of the target, for example, the associated successful observation object or the candidate result of the successful matching.
- the step of updating the step of raising the intuitionistic fuzzy tree and the step of acquiring and predicting the trajectory of the foregoing target are not performed. limit.
- the target image block may also include trajectory information of the target, such as an image block at a position where the trajectory of the reliable target is located, and the step of updating the lifting of the intuitive ambiguous tree at this time should be performed after the step of acquiring the trajectory of the aforementioned target.
- the prediction result of the unassociated reliable target is obtained by online tracking to obtain the candidate result, and the candidate inductive fuzzy tree is used to match the candidate result with the unassociated reliable target. If the matching is successful, the matching is successful.
- the candidate result performs filtering update on the prediction result of the reliable target to obtain its trajectory, so that in the case where the missed detection occurs and the target cannot find the associated observation object, the enhanced intuitionistic fuzzy tree can be used to find the matching it can be used for Candidate knot for track filter update Therefore, the accuracy of the target trajectory is improved, and the performance of the target tracking is improved.
- the second embodiment of the present invention is based on the object tracking method for improving the intuitionistic fuzzy tree.
- the candidate result is used as the classification prediction value f a (x of the test sample).
- c is defined as:
- T is the number of shallow intuitionistic fuzzy decision trees in the intuitionistic fuzzy tree
- a t is the weight of the tth shallow intuitionistic fuzzy decision tree, which is determined during the process of generating the intuitionistic fuzzy tree.
- G t (x c ) is the classification result obtained by classifying the test sample x c by the t-th shallow intuitionistic fuzzy decision tree, and is defined as:
- ⁇ t (c m
- x c ) is the intuitionistic fuzzy membership degree of the test sample w calculated by the t-th shallow intuitionistic fuzzy decision tree belonging to the reliable target class m.
- ⁇ t (c m
- x c ) is defined as:
- t B is the set of all leaf nodes x c reaches a test sample consisting of a leaf node is reached the test sample b x c, h% (x c ) is a leaf node b as the current node, the test sample x c belongs to the intuitionistic fuzzy membership of the current node,
- the confidence level for the leaf node b to predict the class m is defined as:
- x j is the training sample reaching the leaf node b
- n b there are n b
- c j is the category of the training sample x j
- ⁇ ( ⁇ ) is the Dirac function
- h % (x j ) is the leaf node b as the current node
- the training sample x j belongs to the intuitionistic fuzzy membership of the current node.
- the intuitionistic fuzzy membership degree of the training sample and the test sample belonging to the current node is calculated in the same way.
- the sample x belongs to the current node's intuitionistic fuzzy membership degree h % (x) as the output of all the branch nodes that belong to the current node.
- the product of the intuitionistic fuzzy membership of the path specifically defined as:
- D is the set of all branch nodes that the sample passes before reaching the current node
- d is a branch node in the set
- l represents the output left branch of the branch node
- r represents the output right branch of the branch node
- the sample includes test samples and training samples.
- Intuitionistic fuzzy membership of the output path of the branch node d that the sample passes to reach the current node defined as:
- the output path of the branch node d that should pass according to the sample is the left branch or the right branch, from equation (6) with Select one of the corresponding expressions in the expression.
- h(x d ) is the intuitionistic fuzzy output decision function of the branch node d.
- branch nodes of the traditional binary decision tree adopt hard decision, and the branch node output decision function is defined as:
- x d is the eigenvalue of the sample x of the branch node d
- ⁇ is the feature threshold.
- 0 corresponds to the branch node output left branch
- 1 corresponds branch node output right branch.
- the conventional hard decision function represented by the equation (24) is blurred using a sigmoid function (i.e., a Sigmoid function).
- x d is the eigenvalue of the sample x of the branch node d
- ⁇ is the characteristic threshold
- ⁇ is a constant parameter for controlling the degree of tilt of the Sigmoid function
- ⁇ is the standard deviation of the eigenvalue.
- the dotted line in the figure represents the hard decision function defined by equation (24), and its output jumps at the feature threshold; the solid line represents the fuzzy decision function defined by equation (8), and its output monotonically changes continuously according to the eigenvalue of the sample. And equal to 0.5 at the feature threshold.
- the intuitionistic fuzzy point operator is used to further extend the fuzzy decision function based on Sigmoid function to the intuitionistic fuzzy decision function.
- ⁇ A :U ⁇ [0,1] denotes the membership degree of the element u in the set U belonging to A
- ⁇ A :U ⁇ [0,1] ⁇ A (u) represents the element in the set U u belongs to the non-affiliation of A, and for any u:
- the fuzzy intuitionistic index of the element u in the set U belonging to A is defined as:
- the fuzzy intuition index ⁇ A (u) represents the uncertainty information of the element u relative to the intuitionistic fuzzy set A. If the value of ⁇ A (u) is small, it indicates that the membership value of element u belongs to A is relatively accurate; if the value of ⁇ A (u) is large, it means that the membership value of element u belongs to A has greater uncertainty. Sex. Compared with fuzzy sets, intuitionistic fuzzy sets can reflect the information of membership, non-affiliation and fuzzy intuition index, which is beneficial to better deal with the information of uncertainty.
- Intuitionistic fuzzy point operator Transform the intuitionistic fuzzy set A into an intuitionistic fuzzy set with the following fuzzy intuitionistic exponents:
- the intuitionistic fuzzy point operator The fuzzy intuition exponent ⁇ A (u) is divided into: (1- ⁇ u - ⁇ u ) n ⁇ A (u), ⁇ u ⁇ A (u)(1-(1- ⁇ u - ⁇ u ) n )/ ( ⁇ u + ⁇ u ) and ⁇ u ⁇ A (u)(1-(1- ⁇ u - ⁇ u ) n )/( ⁇ u + ⁇ u ) are three parts, which respectively represent the unknown in the original uncertain information. , affiliated and non-affiliated parts.
- Equation (35) shows that the intuitionistic fuzzy point operator
- the fuzzy intuition index of the intuitionistic fuzzy set A can be reduced.
- Intuitive fuzzy point operator New information can be extracted from the uncertainty information of the element u relative to the intuitionistic fuzzy set A, and the degree of utilization of the uncertain information is improved.
- the intuitionistic fuzzy output decision function h(x d ) of the branch node d obtained by the intuitionistic fuzzy generalization is defined as:
- k is the number of operators and is a positive integer.
- ⁇ (z) in equation (7) is a fuzzy intuitionistic index, which is defined as:
- ⁇ is a constant parameter, 0 ⁇ ⁇ ⁇ 1, for example 0.8.
- x d ⁇ ⁇ , z g (x d )
- x d ⁇ ⁇ , z 1 - g (x d ).
- ⁇ in equation (7) is a scale factor for extracting membership information from the fuzzy intuition index
- ⁇ is a scale factor for extracting non-affiliation information from the fuzzy intuition index, which is defined as:
- Equation (7) represents the intuitionistic fuzzy membership of the sample belonging to the right branch of the branch node output.
- Intuitionistic fuzzy point operator It is able to extract new useful information from the uncertain information.
- Equation (7) adds the membership information extracted from the fuzzy intuition index ⁇ (z) to the original fuzzy membership information g(x d ), thereby reducing the original fuzzy membership.
- the graph of the branch node intuitionistic fuzzy output decision function is as shown in FIG. 3.
- the dashed line in the figure represents the fuzzy decision function defined by equation (8); the solid line represents the intuitionistic fuzzy decision function defined by equation (7).
- the value of the operator number k and the feature threshold value ⁇ in the equation (7) can be determined by updating the feature selection criterion in the process of lifting the intuitionistic fuzzy tree, or can be determined by other methods such as taking the empirical value.
- the branch nodes of the traditional binary decision tree adopt hard decision.
- the test sample can only select one of the left and right branches according to the feature attribute to reach the next layer node, and finally reach a leaf node.
- the test sample category is determined by the arriving leaf node. The category is determined.
- Such a hard decision decision tree is not robust to sample noise. When the sample is subjected to strong noise interference, its eigenvalue will change greatly, which may cause the branch of the sample to change and reduce the accuracy of the decision tree.
- fuzzy decision tree In the prior art, a fuzzy decision tree is proposed.
- the fuzzy set theory is applied to the training and reasoning process of decision trees.
- the representation ability of fuzzy set theory is used to improve the processing ability of traditional decision trees for noisy data and incomplete data.
- the fuzzy decision tree can process the eigenvalues with uncertainty, it needs to perform fuzzy semantic processing on the sample features, and the sample features used in the target tracking are mostly numerical features, and the feature dimension is high, which makes Fuzzy semantic processing of sample features becomes difficult.
- the intuitionistic fuzzy decision tree uses the Sigmoid function to obfuscate the hard decision of the traditional decision tree, omitting
- the complex fuzzy semantic process is used, and the intuitionistic fuzzy point operator is used to generalize the fuzzy membership degree to the intuitionistic fuzzy membership degree, extract useful information and improve the robustness.
- the depth of the intuitionistic fuzzy decision tree increases, the amount of storage and computation required to construct the intuitionistic fuzzy decision tree grow exponentially.
- a shallow intuitionistic fuzzy decision tree with a smaller maximum depth can be used.
- AshaBoost algorithm is used to combine multiple shallow intuitionistic fuzzy decision trees into strong classifiers, that is, to enhance intuitionistic fuzzy trees.
- the improved intuitionistic fuzzy tree overcomes the shortcomings of the shallow intuitionistic fuzzy decision tree with weak classification performance, and has the ability to process the uncertainty information in the sample eigenvalues.
- the third embodiment of the present invention is based on the object tracking method for improving the intuitionistic fuzzy tree, which is based on the first embodiment of the target tracking method based on the improved intuitionistic fuzzy tree, and is successful for association or matching.
- Reliable target updates to enhance intuitionistic fuzzy trees include:
- a reliable target with successful association or matching success is added to the positive training sample set as a new positive training sample in the current video frame, and several image blocks are selected as negative training samples within a specified range around the positive training sample.
- the training sample set and the negative training sample constitute a training sample set.
- the positive training sample set in this embodiment may include all corresponding image blocks in the current and previous video frames of the reliable target, and may also limit the number of positive training samples in the positive training sample set to be less than or equal to a specified threshold to save storage resources.
- the resulting set of training samples W ⁇ (x 1 , c 1 ), (x 2 , c 2 ), ..., (x n , c n ) ⁇ , where x j is the training sample and c j is the training sample
- the classification error rate e t on the training sample set W is defined as:
- the weight a t of the generated t-th shallow intuitionistic fuzzy decision tree is defined as:
- the updated weights w t+1,j of the training samples used to generate the t+1th shallow intuitionistic fuzzy decision tree are:
- step S43 the process returns to step S43 to continue the loop.
- This embodiment can be combined with the second embodiment of the target tracking method based on the improved intuitionistic fuzzy tree of the present invention.
- the fourth embodiment of the present invention is based on the third embodiment of the object tracking method for improving the intuitionistic fuzzy tree.
- the step S44 specifically includes:
- S441 Initialize the intuitionistic fuzzy membership of the training sample belonging to the root node in the training sample set.
- the weight of the training sample in the training sample set is taken as the intuitionistic fuzzy membership degree of the training sample belonging to the root node.
- S442 Perform feature selection criterion training on the training samples reaching the current node.
- the initial current node is the root node.
- the optimal one-dimensional feature of the current node and the operator number of the optimal one-dimensional feature and the value of the feature threshold are confirmed.
- the optimal one-dimensional feature belongs to the high-dimensional feature vector of the training sample.
- Stop conditions can include:
- the depth of the current node in the shallow intuitionistic fuzzy decision tree reaches a fifth threshold ⁇ d .
- the fifth threshold ⁇ d is a small positive integer. For example, when the depth of the root node is 0, ⁇ d may take 2.
- step S444 If any one of the above three conditions is satisfied, the process proceeds to step S444, and if none of them is satisfied, the process proceeds to step S445.
- S445 Split the current node to generate two branch nodes of the next layer by using the optimal one-dimensional feature.
- the process of generating a shallow intuitionistic fuzzy decision tree is a process of recursively constructing a binary tree starting from the root node and maximizing the gain of intuitionistic fuzzy information as a feature selection criterion.
- step S442 specifically includes:
- S410 randomly select a one-dimensional feature from the high-dimensional feature vector of the training sample.
- S420 Select one of the candidate feature thresholds, calculate the intuitionistic fuzzy information gain when the number of operators takes different values under the selected one-dimensional feature and the feature threshold, and record the selected one-dimensional feature and feature threshold. The value, the maximum intuitionistic fuzzy information gain, and the value of the corresponding operator number.
- the candidate feature threshold may include a median value of two adjacent values obtained by sorting the values of the selected one-dimensional features of the training sample, and n training samples may obtain n- 1 median.
- the candidate feature threshold may also include an average of the values of the selected one-dimensional features of all training samples. Of course, it can also be a combination of the above two.
- the intuitionistic fuzzy information gain ⁇ H is defined as:
- the training samples can pass through each output path of each branch node to each node in the shallow intuitionistic fuzzy decision tree, so X is substantially the same as the training sample set W.
- H(X) is the intuitionistic fuzzy entropy of set X, defined as:
- ⁇ ( ⁇ ) is the Dirac function
- c j is the category label of the training sample
- the definition and calculation of the intuitionistic fuzzy membership degree h % (x j ) of the training sample belonging to the current node can be referred to the equations (5)-(10). It should be noted that the samples in the equations (5)-(10) at this time x is the training sample in the set X, and the sample features, feature thresholds, and number of operators used are belonging to the branch nodes before reaching the current node.
- H l (X) is the intuitionistic fuzzy entropy of the set of training samples contained in the left branch of the current node output, defined as:
- H r (X) is the intuitionistic fuzzy entropy of the set of training samples contained in the right branch of the current node output, defined as:
- the intuitionistic fuzzy information gain ⁇ H is calculated separately when the number of operators is taken, and the largest intuitionistic fuzzy information gain ⁇ H is found therefrom for recording.
- step S430 Perform a previous step for each of the candidate feature thresholds (ie, step S420), and find and save the one with the largest gain of the intuitionistic fuzzy information in all the records.
- the one-dimensional feature included in the record is the optimal one-dimensional feature.
- the value of the feature threshold and the number of operators are the number of operators and the value of the feature threshold of the optimal one-dimensional feature. .
- the fifth embodiment of the present invention is based on the third embodiment of the object tracking method for improving the intuitionistic fuzzy tree.
- the step S44 specifically includes:
- S446 Initialize the intuitionistic fuzzy membership degree of the training sample belonging to the root node in the training sample set.
- the initial current node is the root node.
- step S448 If any of the above three conditions is satisfied, the process proceeds to step S448, and if none of them is satisfied, the process proceeds to step S449.
- S449 Perform feature selection criterion training on the training samples reaching the current node, and use the optimal one-dimensional feature to split the current node to generate two branch nodes of the next layer.
- the branch node is returned as the current node to step S447 to continue execution until all the current nodes become leaf nodes, and no branch nodes are generated.
- the difference between the present embodiment and the fourth embodiment of the present invention based on the objective tracking method for improving the intuitionistic fuzzy tree is that the step of determining whether the current node satisfies the stop condition and the step of performing the feature selection criterion training for the training sample reaching the current node are different.
- the fourth embodiment of the target tracking method based on the improved intuitionistic fuzzy tree of the present invention and details are not described herein again.
- the sixth embodiment of the present invention is based on the method for improving the target tracking method of the intuitionistic fuzzy tree.
- the step S2 includes:
- the similarity measure includes a spatial distance feature similarity measure and an appearance feature similarity measure.
- the spatial distance feature is one of the features that can more effectively match the observation and prediction results of the target.
- the spatial distance feature similarity measure ⁇ 1 between the observation d and the prediction result o is defined as:
- the target template set is used to represent the appearance of the target.
- the result set corresponding to the prediction result o is
- the total number of target templates included. For convenience of storage and calculation, the number of target templates included in the target template set is limited, and n 2 is less than or equal to the seventh threshold ⁇ , and ⁇ 5 may be taken.
- the appearance feature similarity measure ⁇ 2 between the observation d and the prediction result o is defined as:
- s( ⁇ ) is a normalized correlation measure between the observation d and the target template e i , defined as:
- d(x, y) is the gray value of the observation result d at the coordinates (x, y)
- e i (x, y) is the gray value of the target template e i at the coordinates (x, y)
- the observation d is also whitened and scaled to h ⁇ w.
- the value of s ranges from [0,1].
- the associated cost between the observation d and the predicted result o is defined as:
- the cost is defined as:
- ⁇ ij is the correlation cost between the observation d i defined by equation (17) and the prediction result o j
- the correlation matrix A 0 which minimizes the total associated cost of the observation result and the prediction result is the optimal correlation matrix.
- the Hungarian algorithm can be used to solve the correlation results.
- the seventh embodiment of the present invention is based on the sixth embodiment of the object tracking method based on the improved intuitionistic fuzzy tree, and the step S3 is After further including:
- the associated/matching object image block with the reliable target current frame association success or matching success is whitened and scaled to h ⁇ w and then added to the target template set of the reliable target. If the number of target templates in the target template set before joining is equal to the seventh threshold, the earliest added target template in the target template set is deleted.
- step S4 may be independent of each other or may be performed simultaneously.
- the first embodiment of the present invention is based on a target tracking device for improving an intuitionistic fuzzy tree.
- the first embodiment includes a processor 110 and a camera 120.
- the camera 120 can be a local camera, the processor 110 is connected to the camera 120 via a bus; the camera 120 can also be a remote camera, and the processor 110 is connected to the camera 120 via a local area network or the Internet.
- the processor 110 controls the operation of the target tracking device based on the lifting of the intuitionistic fuzzy tree, which may also be referred to as a CPU (Central Processing Unit).
- Processor 110 may be an integrated circuit chip with signal processing capabilities.
- the processor 110 can also be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, and discrete hardware components.
- the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
- the target tracking device based on the enhanced intuitionistic fuzzy tree may further include a memory (not shown) for storing instructions and data necessary for the operation of the processor 110, and may also store video data captured by the transmitter 120.
- the processor 110 is configured to perform motion detection on the current video frame acquired from the camera 120, detect the obtained possible moving object as an observation result, and associate the observation result with the prediction result of the target, wherein the prediction result is at least using the previous video frame.
- the target trajectory is predicted, and the target includes a reliable target and a temporary target; the trajectory management is performed on the unrelated observation result and the predicted result, which includes online tracking of the uncorrelated reliable target to obtain the candidate result,
- the candidate inductive fuzzy tree is used to match the candidate results by using the improved intuitionistic fuzzy tree; the correlation result and the matching result are used to obtain the trajectory of the target of the current frame, including the prediction that the successful target with successful matching is predicted by the candidate with successful matching.
- the result is filtered to update the trajectory; the trajectory of the target of the current frame is used for prediction, and the intuitionistic fuzzy tree is updated for the reliable target update with successful association or matching.
- each part of the target tracking device for improving the intuitionistic fuzzy tree of the present invention can be referred to the description in the corresponding embodiments of the online target tracking method of the present invention, and details are not described herein again.
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- 一种基于提升直觉模糊树的目标跟踪方法,其中,包括:对当前视频帧进行运动检测,检测得到的可能运动对象作为观测结果;对所述观测结果和目标的预测结果进行关联,其中所述预测结果是至少利用前一视频帧的目标的轨迹进行预测而得到的,所述目标包括可靠目标及临时目标;对未被关联的所述观测结果和所述预测结果进行轨迹管理,其中包括对未被关联的所述可靠目标的预测结果进行在线跟踪获取候选结果,利用所述未被关联的所述可靠目标的提升直觉模糊树对所述候选结果进行匹配;利用关联结果和匹配结果获取当前帧的目标的轨迹,其中包括对匹配成功的所述可靠目标利用其匹配成功的所述候选结果对其预测结果进行滤波更新以获取所述轨迹;利用所述当前帧的目标的轨迹进行预测,并为关联成功或匹配成功的所述可靠目标更新所述提升直觉模糊树。
- 根据权利要求1所述的方法,其中,所述对未被关联的所述可靠目标的预测结果进行在线跟踪获取候选结果包括:在所述可靠目标的预测结果位置及其周围指定范围内选择若干个图像块作为所述候选结果,所述候选结果可以包括未被关联的所述观测结果;所述利用所述未被关联的所述可靠目标的提升直觉模糊树对所述候选结果进行匹配包括:利用所述提升直觉模糊树计算所述候选结果作为测试样本的分类预测值;若所述分类预测值大于第一阈值,且所述候选结果与所述可靠目标的预测结果的外观特征相似性度量大于第二阈值,则匹配成功。
- 根据权利要求2所述的方法,其中,所述候选结果作为测试样本的分类预测值fa(xc)定义为:其中xc为所述测试样本,T为所述提升直觉模糊树中浅层直觉模糊决策树的个数,at为第t个所述浅层直觉模糊决策树的权重;Gt(xc)为第t个所述浅层直觉模糊决策树对所述测试样本xc进行分类得到的分类结果,定义为:其中Gt(xc)=1表示所述测试样本属于所述可靠目标类别,Gt(xc)=-1表示所述测试样本属于非所述可靠目标类别,φt(c=m|xc)为利用第t个所述浅层直觉模糊决策树计算得到的所述测试样本xc隶属于所述可靠目标类别m的直觉模糊隶属度,定义为:c为所述测试样本的类别标签,m为所述可靠目标类别,在所述第t个所述浅层直觉模糊决策树中,Bt为所述测试样本xc到达的所有叶子节点构成的集合,b为所述测试样本xc到达的一个叶子节点,h%(xc)为将所述叶子节点b作为当前节点时所述测试样本xc隶属于所述当前节点的直觉模糊隶属度,为所述叶子节点b预测类别为m的置信度,定义为:其中xj为到达所述叶子节点b的训练样本,共有nb个,cj为所述训练样本xj的类别标签,δ(·)为狄拉克函数,h%(xj)为将所述叶子节点b作为当前节点时所述训练样本xj隶属于所述当前节点的直觉模糊隶属度。
- 根据权利要求3所述的方法,其中,样本x隶属于当前节点的直觉模糊隶属度定义h%(x)定义为:所述样本包括所述测试样本和所述训练样本,若所述当前节点为根节点,当所述样本x为所述测试样本时,h%(x)=1,当所述样本x为所述训练样本时,h%(x)等于所述样本x的权值;其中D为所述样本到达所述当前节点之前经过的所有分支节点的集合,d为所述集合中的一个分支节点,l表示所述分支节点的输出左分支,r表示所述分支节点的输出右分支,为所述样本到达所述当前节点所经过的隶属于所述分支节点d的输出路径的直觉模糊隶属度,定义为:其中xd为所述分支节点的所述样本的特征值,k为算子次数,为正整数;g(xd)为S型函数,定义为:其中τ为特征门限值,θ为用于控制所述S型函数倾斜程度的常量参数,σ为所述特征值的标准差;π(·)为模糊直觉指数,定义为:其中λ为常量参数,0<λ<1,当xd≥τ时,z=g(xd),当xd<τ时,z=1-g(xd);α为从模糊直觉指数中提取隶属信息的尺度因子,β为从模糊直觉指数中提取非隶属信息的尺度因子,定义为:β=1-α-π(z)其中所述算子次数k和所述特征门限值τ的取值通过更新所述浅层直觉模糊决策树过程中的特征选择准则训练确定。
- 根据权利要求1所述的方法,其中,所述为关联成功或匹配成功的所述可靠目标更新所述提升直觉模糊树包括:将所述关联成功或匹配成功的所述可靠目标在所述当前视频帧中对应的目标图像块作为新的正训练样本加入正训练样本集中,在所述正训练样本周围指定范围内选择若干个图像块作为负训练样本,利用所述正训练样本集和所述负训练样本组成的训练样本集合生成新的所述提升直觉模糊树。
- 根据权利要求5所述的方法,其中,所述利用所述正训练样本和所述负训练样本组成的训练样本集合生成新的所述提升直觉模糊树包括:初始化所述训练样本集合中的训练样本的权值;以所述训练样本的权值作为所述训练样本隶属于根节点的直觉模糊隶属度,利用所述训练样本集合生成浅层直觉模糊决策树,计算所述浅层直觉模糊决策树在所述训练样本集合上的分类误差率,利用所述分类误差率计算所述浅层直觉模糊决策树的权重,利用所述权重更新所述训练样本的权值以获取下一个所述浅层直觉模糊决策树的所述训练样本的权值;循环执行前一步骤直至所述分类误差率小于预设阈值或者生成的所述浅层直觉模糊决策树个数达到预设数量,生成的所述浅层直觉模糊决策树组成所述新的提升直觉模糊树。
- 根据权利要求6所述的方法,其中,所述初始化所述训练样本集合中的训练样本的权值包括:将每个训练样本的权值初始化为1/n,其中n为所述训练样本集合中训练样本的个数;所述利用所述训练样本集合生成浅层直觉模糊决策树包括:以所述训练样本集合中训练样本的权值作为所述训练样本隶属于所述根节点的直觉模糊隶属度;对到达当前节点的所述训练样本进行特征选择准则训练,根据直觉模糊信息增益最大原则确认当前节点的最优一维特征及所述最优一维特征的算子次数和特征门限值的取值,其中所述最优一维特征属于所述训练样本的高维特征矢量,然后判断所述当前节点是否满足停止条件,若满足,则将所述当前节点转化为叶子节点,若不满足,则使用所述最优一维特征将所述当前节点分裂生成下一层的两个分支节点;或判断所述当前节点是否满足停止条件,若满足,则将所述当前节点转化为叶子节点,若不满足,则对到达当前节点的所述训练样本进行所述特征选择准则训练,然后使用所述最优一维特征将所述当前节点分裂生成下一层的两个分支节点;将所述分支节点作为当前节点返回前一步骤继续执行。
- 根据权利要求7所述的方法,其中,所述对到达当前节点的所述训练样本进行特征选择准则训练包括:从所述训练样本的高维特征矢量中随机选择一个一维特征;从候选特征门限值中选择一个,在选中的一维特征和特征门限值条件下计算所述算子次数取不同数值时的直觉模糊信息增益,记录所述选中的一维特征、所述特征门限值的取值、最大的所述直觉模糊信息增益以及对应的所述算子次数的取值;为所述候选特征门限值中的每一个执行前一步骤,找出并保存所有记录中所述直觉模糊信息增益最大的一条;重复执行上述步骤指定次数,在获取的所有保存的记录中找出所述直觉模糊信息增益最大的一条,其中包括的所述一维特征为所述最优一维特征,所述特征门限值的取值和所述算子次数的取值为所述最优一维特征的算子次数和特征门限值的取值。
- 根据权利要求8所述的方法,其中,所述直觉模糊信息增益ΔH定义为:其中X={x1,x2,...,xn}为到达所述当前节点的训练样本的集合,n为所述集合中所述训练样本的个数;H(X)为所述集合X的直觉模糊熵,定义为:其中δ(·)为狄拉克函数,cj为所述训练样本的类别标签,mi为所述训练样本的类别,由于仅需要对属于目标和不属于目标进行区分,因此共有两类,i=1,2;训练样本隶属于所述当前节点的直觉模糊隶属度h%(·)定义为:其中D为所述训练样本到达所述当前节点之前经过的所有分支节点的集合,d为所述集合中的一个分支节点,为所述训练样本隶属于所述分支节点d的输出左分支的直觉模糊隶属度,为所述训练样本隶属于所述分支节点d的输出右分支的直觉模糊隶属度,定义为:其中h(xd)为所述分支节点d的直觉模糊输出判决函数,定义为:其中xd为所述分支节点的所述训练样本的特征值,k为所述算子次数,为正整数;g(xd)为S型函数,定义为:其中τ为所述特征门限值,θ为用于控制所述S型函数倾斜程度的常量参数,σ为所述特征值的标准差;π(·)为模糊直觉指数,定义为:其中λ为常量参数,0<λ<1,当xd≥τ时,z=g(xd),当xd<τ时,z=1-g(xd);α为从模糊直觉指数中提取隶属信息的尺度因子,β为从模糊直觉指数中提取非隶属信息的尺度因子,定义为:β=1-α-π(z)Hl(X)为所述当前节点输出左分支所包含的训练样本的集合的直觉模糊熵,定义为:Hr(X)为所述当前节点输出右分支所包含的训练样本的集合的直觉模糊熵,定义为:
- 根据权利要求9所述的方法,其中,所述计算所述浅层直觉模糊决策树在所述训练样本集合上的分类误差率包括:对于已生成的第t个所述浅层直觉模糊决策树,其在所述训练样本集合W={(x1,c1),(x2,c2),...,(xn,cn)}上的分类误差率et定义为:其中xj为所述训练样本,cj为所述训练样本的类别标签,cj∈{-1,1},j=1,2,...,n,cj=1表示所述训练样本属于所述可靠目标类别为所述正训练样本,cj=-1表示所述训练样本属于非所述可靠目标类别为所述负训练样本;wt,j,j=1,2,...,n为生成第t个所述浅层直觉模糊决策树所用的所述训练样本的权值,I(·)为指示函数,Gt(xj)为已生成的第t个所述浅层直觉模糊决策树对所述训练样本xj进行分类得到的分类结果;所述利用所述分类误差率计算所述浅层直觉模糊决策树的权重包括:已生成的第t个所述浅层直觉模糊决策树的权重at定义为:所述利用所述权重更新所述训练样本的权值包括:用于生成第t+1个所述浅层直觉模糊决策树的所述训练样本的权值wt+1,j为:其中Zt为归一化因子,计算公式为:
- 根据权利要求8所述的方法,其中,所述候选特征门限值包括对所述训练样本的所述选中的一维特征的取值进行排序后得到的相邻两个所述取值的中值。
- 根据权利要求8所述的方法,其中,所述候选特征门限值包括所有所述训练样本的所述选中的一维特征的取值的平均值。
- 根据权利要求7所述的方法,其中,所述停止条件包括:到达所述当前节点某一类别的所述训练样本隶属于所述当前节点的直觉模糊隶属度的和占到达所述当前节点全部所述训练样本的直觉模糊隶属度的总和 的比重大于第三阈值;或到达所述当前节点的所述训练样本隶属于所述当前节点的直觉模糊隶属度的总和小于第四阈值;或所述当前节点在所述浅层直觉模糊决策树中的深度达到第五阈值。
- 根据权利要求1-13中任一项所述的方法,其中,对未被关联的所述观测结果和所述预测结果进行轨迹管理进一步包括:为未被关联且不是匹配成功的候选结果的所述观测结果建立新的临时目标,将连续关联成功的帧数大于第一帧数阈值的所述临时目标变为可靠目标,删除连续关联不成功的帧数大于第二帧数阈值的所述临时目标,删除连续关联不成功的帧数大于第三帧数阈值,且所述匹配结果为匹配失败的所述可靠目标,其中所述匹配结果为匹配失败是指利用所述提升直觉模糊树计算所述候选结果作为测试样本隶属于所述可靠目标类别的直觉模糊隶属度小于或者等于第六阈值。
- 根据权利要求14所述的方法,其中,所述利用关联结果和匹配结果获取当前帧的目标的轨迹进一步包括:对关联成功的所述目标利用其关联的所述观测结果对其预测结果进行滤波更新以获取所述轨迹,对所述新的临时目标将对应的所述观测结果作为所述轨迹,对关联不成功且未被删除的所述临时目标以及关联不成功且匹配不成功且未被删除的所述可靠目标将其预测结果作为所述轨迹。
- 根据权利要求1-13中任一项所述的方法,其中,所述对所述观测结果和目标的预测结果进行关联包括:计算所述观测结果和所述预测结果之间的相似性度量,所述相似性度量包括空间距离特征相似性度量以及外观特征相似性度量;利用所述相似性度量计算所述观测结果和所述预测结果之间的关联代价;利用所述关联代价计算所述观测结果和所述预测结果之间的最优关联矩阵作为关联结果,使得所述观测结果和所述预测结果的总关联代价最小。
- 根据权利要求16所述的方法,其中,包括:观测结果d与预测结果o之间的所述空间距离特征相似性度量ψ1定义为:所述预测结果o对应的目标模板集为其中的目标模板 ei,i=1,...,n2为经过白化处理且大小缩放至h×w的之前n2个视频帧中的关联/匹配对象图像块,n2为所述目标模板集中包括的所述目标模板的总数且小于或等于第七阈值,所述观测结果d与所述预测结果o之间的所述外观特征相似性度量ψ2定义为:其中s(·)为所述观测结果d与所述目标模板ei之间的归一化相关性度量,定义为:其中d(x,y)为所述观测结果d在坐标(x,y)处的灰度值,ei(x,y)为所述目标模板在坐标(x,y)处ei的灰度值;所述观测结果d与所述预测结果o之间的关联代价定义为:ρo,d=1-ψ1×ψ2 (22)所有所述观测结果组成的集合为D={d1,...,dp},所有所述预测结果组成的集合为O={o1,...,oq},所述观测结果和所述预测结果的总关联代价定义为:
- 根据权利要求17所述的方法,其中,进一步包括:对于关联成功或匹配成功的所述可靠目标,将其关联/匹配对象图像块经过白化处理且大小缩放至h×w之后加入所述可靠目标的所述目标模板集中,并且若加入之前所述目标模板集中所述目标模板的数量等于所述第七阈值,删除所述目标模板集中最早加入的所述目标模板。
- 根据权利要求1-13中任一项所述的方法,其中,所述利用所述当前帧的目标的轨迹进行预测包括:利用卡尔曼滤波器对所述当前帧的目标的轨迹进行预测以获取下一帧的目标的预测结果。
- 一种基于提升直觉模糊树的目标跟踪装置,其中,包括:处理器和摄像机,所述处理器连接所述摄像机;所述处理器用于对从所述摄像机获取的当前视频帧进行运动检测,检测得到的可能运动对象作为观测结果;对所述观测结果和目标的预测结果进行关联,其中所述预测结果是至少利用前一视频帧的目标的轨迹进行预测而得到的,所述目标包括可靠目标及临时目标;对未被关联的所述观测结果和所述预测结果进行轨迹管理,其中包括对未被关联的所述可靠目标的预测结果进行在线跟踪获取候选结果,利用所述未被关联的所述可靠目标的提升直觉模糊树对所述候选结果进行匹配;利用关联结果和匹配结果获取当前帧的目标的轨迹,其中包括对匹配成功的所述可靠目标利用其匹配成功的所述候选结果对其预测结果进行滤波更新以获取所述轨迹;利用所述当前帧的目标的轨迹进行预测,并为关联成功或匹配成功的所述可靠目标更新所述提升直觉模糊树。
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