CN100587719C - Method for tracking dimension self-adaptation video target with low complex degree - Google Patents
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Abstract
A scale self-adapting video target tracking method with low complexity in the video intelligent monitoring technical field, including: initializing the state of the particle sample; randomly generating two scale factors on each sampling point for the particle sample, computing the sample second autoegression center, storing as the mean drifting center; obtaining a mean drift field with neighborhood uniformity according to the second autoegression center of the sampling point in the sample set; building the important sampling density function for each sampling point, and obtaining the updatingstate X1' of the sample combining with the mean drifting method from the sampled probability angle of the Monte Carto, and updating the weight value w' under the state X1', then sampling again on thesample set {x1', w1'}, wherein 1=1...N, to obtain the posterior probability distribution dispersion estimation set {x1', w1'} of the object final state at the moment, wherein 1=1...N. The invention advances the tracking accuracy of the object scale space, reduces the computing complexity in the realtime video tracking.
Description
Technical Field
The invention relates to a method in the technical field of intelligent video monitoring, in particular to a low-complexity scale self-adaptive video target tracking method.
Background
In many applications in the field of computer vision, such as intelligent monitoring, robot vision, and human-computer interaction interfaces, a moving target between frames of a video sequence needs to be tracked. Due to the diversity of the forms of the tracked targets and the uncertainty of the target motion, how to realize robust real-time tracking in various environments and realize reliable estimation of the variable scale of the tracked targets along with the change of the target distance is always a hot point of research. The sequential monte carlo filtering method is a widely used tracking method in recent years, and represents the most likely state of a target, such as position, size and the like, by the posterior probability distribution of the target in a state space. Unlike conventional kalman filtering, this method approximates the true target state distribution function with a set of discrete sample points, and is therefore also referred to as particle filtering. The sequential monte carlo filtering method has the advantages that the sequential monte carlo filtering method is suitable for a non-gaussian non-linear system, but the application of the sequential monte carlo filtering method in a real-time system is limited due to large calculation amount. Mean shift is another common target tracking method that locally iteratively searches for the most likely states that a target has in an image along the gradient direction of the target state probability function. In 1998, Bradski et al improved the basic mean shift method, and used the higher order moments of the probability distribution of the target state to obtain a more accurate target scale size while searching for the target position, i.e., a scale-adaptive mean shift method. The method has high operation speed, but the method can be converged to a local optimal point of a state space, so that the phenomenon of target tracking loss is easy to occur under the conditions of shielding, similar background interference and the like.
Based on the respective advantages of the sequential monte carlo filtering and the mean shift method, Shan et al propose a particle filtering method with embedded mean shift. According to the method, independent mean shift is carried out on each sampling point in the sequential Monte Carlo filter, so that the sampling points are more concentrated on a local optimal value, the sampling efficiency is improved, and the operation complexity is reduced.
Similar methods have been used and improved in recent years as found by the search of prior art documents, such as "Object tracking by the mean-shift of the regional color distribution combined with the particle-filter target tracking method", published by Koichiro et al in the seventeenth International Conference on Pattern recognition, pages 506 to 509 of the seventeenth International Conference on Pattern recognition, 2004. In the method combining the particle filtering and the mean shift, the mean shift of each particle subjected to state prediction is optimized, so that the obtained sampling point set is closer to a state space with high mode probability, the sampling efficiency is improved, and the required particle number is reduced. On the other hand, the operation time of the particle filter is directly determined by the number of particles, so that the improvement simultaneously increases the speed of the tracker. However, these improvements also present some drawbacks: the particles are too concentrated to cause sample starvation, so that the diversity of sequential monte carlo filtering is destroyed. The root of the method lies in that the method for combining the sequential Monte Carlo filtering and the mean shift in the work is too simple, the influence of the embedded mean shift optimization means on the original sequential Monte Carlo filtering frame is not considered, and the method is mainly characterized in that the particle state space presents new probability distribution. Therefore, a complete mean shift combination method is provided from the perspective of the Monte Carlo sampling probability, and the method effectively reduces the estimation error of the target scale along with the change of the target size in the tracking process, and is blank at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a low-complexity scale self-adaptive video target tracking method, which directly fuses a scale self-adaptive mean shift method to a probability system of sequential Monte Carlo filtering by designing an importance sampling function, namely, the optimization effect of the mean shift method on sampling points is embodied in the importance sampling function, and the estimation of the probability distribution of a target state is ensured to be unbiased.
The invention is realized by the following technical scheme, and the invention comprises the following steps:
step one, initializing a particle sample state: determination of the initial state x of an object by detection0Simultaneously calculating the observation vector of the target, giving the number N of the particle sampling points, and setting the initial state vectors of all the sampling points to be x0And are given uniform weights
Step two, the particle sample in step one is obtained by taking uniformly distributed random variables U-U [0, 1 ] for each sampling point i as 1: N, if
The scale factor k of the sampling pointiIs taken to be k1OtherwiseIs taken to be k2Wherein: k is a radical of1＝1.2，k2Respectively representing the variation characteristics of scale enlargement and scale reduction of the target between frames, calculating a second-order autoregressive central point of the sample, and storing the second-order autoregressive central point as a central point of mean shift;
the calculating of the second-order autoregressive central point of the sample specifically comprises the following steps:
let the current sampling point set be { xt i，wt i}i＝1...NCalculating the second-order autoregressive center of each sampling point i as 1: N
In the formula, xt iA state vector representing the ith particle at time t;
step three, obtaining a mean shift field with neighborhood consistency according to the second-order autoregressive center of the sampling points in the sample set in the step two, wherein the mean shift field with neighborhood consistency is as follows:
first, each sampling point i is judged to be 1: N, and if i is 1 or
In thatOutside the neighborhood omega, sequentially executing the second, third, fifth and sixth steps, or else, sequentially executing the fourth, fifth and sixth steps; wherein,
respectively being a second-order autoregressive center of the sampling points i and i-1 at the moment t;
② reference stateWherein p isref、srefRespectively position and scale sub-states, sref＝{lref，href}，lref，hrefRespectively describe with prefThe length and height of the target rectangular outline at the center; zero order moment cumulant m00(xrefN is 0) is 1, n is 0: I;
c, circulating n-1: I:
first, a mean shift vector M is calculatedr，g(xref)：
Wherein，{pj}j＝1...MIs the reference state xrefPixel coordinate, m (p), covered by a rectangular area of the imagej) Is position pjWhen the observation vector is a color histogram, the probability weight of (2) is determined by xrefThe matching degree of a color interval between a target color histogram corresponding to the state and a target feature template is obtained, g (-) is a kernel function, and r is a factor for normalizing kernel function envelope;
then, the position sub-state of the reference state is updated:
pref＝pref+Mr，g(xref)
calculating the zeroth order moment density of the reference state:
wherein: m00(xref) Is that the target appears in state xrefAnd updating the zero order moment cumulant in the corresponding rectangular region through the zero order moment density:
m00(xref，n)＝m00(xref，n-1)×m00(xref)
respectively assigning the mean value drift vector, the zeroth order moment density and the zeroth order moment cumulant of the ith sampling point directly according to corresponding information of the ith-1 sampling point;
fifthly, obtaining the final zero-order moment cumulant
And a new state through mean shift optimization and scale adaptive adjustment:
storing the end position of the mean shift
And final zeroth moment cumulant
Step four, establishing an importance sampling density function for each sampling point, and combining the probability angle of Monte Carlo sampling with the mean shift method in the step three to obtain the update state x of the samplet iAnd in state xt iLower update weight wt iAnd then by setting the sampling points { x }t i，wt i}i＝1...NResampling is carried out to obtain discrete estimation { x) of posterior probability distribution of the target final state at the current momentt i，wt i}i＝1...N。
Establishing an importance sampling density function for each sampling point specifically comprises the following steps:
4.1) randomly dividing the set of particles into two groups, wherein the first group accounts for a proportion of alpha and the second group accounts for a proportion of 1-alpha;
4.2) the states of the particles in the first group are transferred according to a second-order autoregressive model, i.e. the state transfer quantity at the previous moment and a random quantity following a Gaussian distribution having the same dimensionality as the states of the particles are added to the current states of the particles, the mean value is zero, and the variance ΣDGiven empirically or learned from known samples;
4.3) when the state of the second group of particles is sampled, the particles are randomly divided into two groups A and B according to equal probability, the sampling process of the particles of each group is respectively carried out according to a Gaussian distribution, but the position components of the Gaussian distribution centers of the two groups are the convergence positions of the particles through a mean shift method, wherein: the scale component of the group A Gaussian distribution center is selected as a scale-up factor k1Optimization scale of time-adaptive scale method, second-order autoregressive model covariance matrix with variance as superposition scale amplification correction quantity
Covariance matrix sigma added with uncertainty of analog adaptive scale mean shift methodCAM(ii) a The scale component of the Gaussian distribution center of the group B is selected as a scale reduction factor k2Optimization scale of time-adaptive scale method, second-order autoregressive model covariance matrix with variance as reduced correction quantity of superposition scale
Covariance matrix sigma added with uncertainty of analog adaptive scale mean shift methodCAMWhere I is the maximum number of convergence iterations of the mean shift,
is the final zeroth moment cumulative of the ith sample point.
The importance sampling density function is specifically as follows:
wherein, the physical meaning of each parameter is as follows: i ═ 1, 2,. N, where N represents the number of particle sampling points; x is the number oft iRepresenting the state vector of the ith particle at time t, considering only the position substate piSum scale substate st iIn the case of (a) in (b),
xt idenotes xt iIntermediate states before resampling; z is a radical oftRepresents an observation vector at time t; the intermediate state of the position of the ith particle after mean shift optimization isWhile
Representing the final zeroth order moment cumulant in the intermediate state; the parameter I is the maximum convergence iteration number of the embedded scale self-adaptive mean shift; α is the probability that the sampled particle state is optimized by mean shift; n (-) represents a Gaussian distribution function; sigmaDAnd ΣCAMRespectively representing the variances of prediction errors brought by a sequential Monte Carlo filtering second-order autoregressive state transition model and a scale self-adaptive mean shift state transition model to target state prediction; the error generated by the embedded scale self-adaptive mean shift method when estimating the scale sub-state is composed of two scale adjustment factors k1And k2Performing optimal selectivity compensation; t denotes a matrix transposer.
Said in state xt iLower update weight wtiThe method specifically comprises the following steps:
wherein, p (z)t|xt i) Is in a hypothetical state xt iThe probability of the observation model of the lower target can be obtained by adopting the matching degree of the color interval between the color histogram and the target characteristic template; p (x)t i|x0:t-1 t) Is the dynamic model probability of the target, here a second order autoregressive model is used; q (x)t i|x0:t-1 i，zt) The probabilities are sampled for importance.
The set of pairs of sampling points { xt i，wt i}i＝1...NResampling is carried out, and specifically: selecting sampling points with large weight in the sample set, normalizing the weight of all the sampling points to obtain discrete estimation { x ] of posterior probability distribution of the final state of the target at the current momentt i，wt i}i＝1...N。
The principle of the invention is that the sequential Monte Carlo filtering time complexity is higher, so the optimization of the scale self-adaptive mean shift on the target position and scale is introduced into the importance sampling function to improve the sampling efficiency and ensure that the approximation of the target posterior probability is more accurate. In order to further reduce complexity, when the intermediate state space of the sample set is calculated, local consistent processing is carried out on the mean shift parameters of the sampling samples, namely the samples in a neighborhood are considered to converge to the same local optimal point after mean shift optimization. The samples are selected to be subjected to mean shift with a certain probability, so that the situation that all the samples are concentrated at the maximum point of a local mode is avoided, and the diversity of the states of the samples is ensured. Meanwhile, two different scale adjustment factors are randomly adopted for the sample, the target hypothesis area is respectively reduced and amplified, the optimal scale is automatically selected through the weight calculation of the sequential Monte Carlo filtering and the resampling mechanism, and the defect that the single-scale self-adaptive mean shift method is not accurate and universal is overcome.
Compared with the prior art, the invention provides a unified importance sampling function, provides a complete mean shift combination method from the perspective of Monte Carlo sampling probability, and reduces the calculation complexity of mean shift optimization by using the consistent convergence characteristic of neighborhood states; only part of samples are subjected to mean shift, so that the problem of sample shortage is avoided; the selection of the multi-scale adjustment factor in the state convergence process overcomes the problems of inaccurate and universal scale estimation. Aiming at a basic target tracking method, namely sequential Monte Carlo filtering and mean shift, under the same experimental condition, the method can more accurately estimate the scale size of the target, more robustly process the tracking problems of target shielding and the like, and effectively reduce the operation time of the tracking method.
Drawings
FIG. 1 is a flowchart of the operation of the tracking method of the present invention;
FIG. 2 is a diagram of tracking effect of a common embedded mean shift sequential Monte Carlo filtering method in a prior art for adaptively adjusting a scale sub-state under a target scale change;
FIG. 3 is a graph of the tracking effect of the adaptive adjustment of the scale substate of the mesoscale adaptive mean shift method in the prior art when the target scale changes;
FIG. 4 is a diagram of the tracking effect of adaptively adjusting the sub-state of the scale when the target scale changes according to the present invention;
FIG. 5 is a comparison of the embodiment of the present invention and the embedded mean-shift sequential Monte Carlo filter tracking method over computation time.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a process are given, but the scope of the present invention is not limited to the following embodiments.
In this embodiment, a "soccer player" video sequence is subject to target tracking. Is provided withThe counting parameters are set as follows: the total number of particles N is 50; scale adjustment factor k1＝1.2，k20.9; for embedded mean shift optimization, the iteration number I is 2, the occurrence probability α is 0.5, and the consistent convergence neighborhood is defined as
The error variance generated by state optimization is about sigmaCAM＝diag{0.25，0.25，2×10-4，2×10-4The error variance generated by the sequential Monte Carlo filtering on the state estimation is about sigmaD＝diag{1.0，0.25，(5，4；4，5)×10-4}。
As shown in fig. 1, the present embodiment includes the following steps:
step one, initializing a particle sample state: in the first frame of the sequence, determining the centroid position, length and width of the target by an automatic target detection method, thereby establishing an initial state vector x of the target0And setting the initial state vectors of all sampling points as x0And are given the same weightMeanwhile, calculating an observation vector of the target, such as a color histogram, as a reference template of the target feature;
step two, randomly generating two different scale factors for each sampling point, calculating a second-order autoregressive central point of the sample, and storing the second-order autoregressive central point as a central point of mean shift; starting from the second frame, recursively calculating the posterior probability sampling distribution of the current frame target by the state distribution of the previous frame sampling point and the observation vector of the current frame image, specifically as follows:
firstly, obtaining respective second-order autoregressive centers of 1-50 sampling points according to a formula (1), and storing the data serving as the central point of mean shift in an array;
② taking uniformly distributed random variables U-U [0, 1 ] for each sampling point i equal to 1: N, if
The scale factor k of the sample pointiIs taken to be k11.2, otherwise k is taken20.9, which respectively represent the variation characteristics of target scale enlargement and scale reduction between frames;
step three, obtaining a mean shift field with neighborhood consistency according to the second-order autoregressive center of the sampling points in the sample set, wherein the mean shift field with neighborhood consistency is as follows:
calculating the mean value drift vector of the state of the point, the position sub-state of the shifted sampling point, the zero-order moment density and the zero-order moment cumulant of the shifted sampling point in sequence for the second-order autoregressive center of the 1 st sampling point according to the formulas (2) to (5), and carrying out iterative calculation for 2 times;
obtaining a new state of the 1 st sampling point after mean shift and scale self-adaptive adjustment according to a formula (6), and storing the ending position and zero-order moment cumulant of the first sampling point in the new state;
(iii) for the ith sample point (i ═ {2, 3, … N }), if the euclidean distance between the position sub-state in its state vector and the position sub-state of the previous sample point is greater than 2, then step (iv) is performed; otherwise, jumping to the fifth step;
fourthly, sequentially calculating the mean value drift vector, the zero order moment density and the zero order moment cumulant of the point according to the formulas (2) to (5) for the second-order autoregressive center of the ith sampling point, and carrying out iterative calculation for 2 times;
directly assigning the mean shift vector, the zero order moment density and the zero order moment cumulant of the ith sampling point by corresponding information at the ith-1 sampling point respectively;
obtaining a new state of the ith sampling point after mean shift and scale self-adaptive adjustment according to a formula (6), and storing the termination position and zero moment cumulant of the ith sampling point in the new state.
And seventhly, repeatedly executing the third step and the fourth step until 50 sampling points are processed.
Step four, firstly, establishing an importance sampling density function for each sampling point, and combining the probability angle of Monte Carlo sampling with a mean shift method to obtain the update state x of the samplet iAnd in state xt iLower update weight wt iAnd then by setting the sampling points { x }t i，wt i}i＝1...NResampling is carried out to obtain discrete estimation { x) of posterior probability distribution of the target final state at the current momentt i，wt i}i＝1...NThe method comprises the following steps:
firstly, establishing an importance sampling function to obtain a new sampling point set { xt iI ═ 1, 2,.., 50}, the importance sampling function is specifically as follows:
wherein, the physical meaning of each parameter is as follows: i ═ 1, 2,. N, where N represents the number of particle sampling points; x is the number oft iRepresenting the state vector of the ith particle at time t, considering only the position substate piSum scale substate st iIn the case of (a) in (b),
xt idenotes xt iIntermediate states before resampling; z is a radical oftRepresents an observation vector at time t; the intermediate state of the position of the ith particle after mean shift optimization is
While
Representing the zero-order moment cumulant in the intermediate state; the parameter I is the iteration number of the embedded scale self-adaptive mean shift; α is the probability that the sampled particle state is optimized by mean shift; n (-) represents a Gaussian distribution function; sigmaDAnd ΣCAMRespectively representing the variances of prediction errors brought by a sequential Monte Carlo filtering second-order autoregressive state transition model and a scale self-adaptive mean shift state transition model to target state prediction; the error generated by the embedded scale self-adaptive mean shift method when estimating the scale sub-state is composed of two scale adjustment factors k1And k2Performing optimal selectivity compensation; t denotes a matrix transposer.
Secondly, updating the weight of each new sampling point by using a formula (8) to obtain wt i}i＝1...N；
Thirdly, the latest weight values of 50 sampling points are normalized to obtain wt i}i＝1...N；
(iv) resampling the i (i ═ 1, 2, … N) th sample point, i.e. copying or deleting the stateThe number of sampling points in the sample set is
Representing a rounding operation;
fifthly, if the total number of the sampling points in the sample set is less than 50, copying the sampling point with the maximum weight value until the total number of the sampling points is equal to 50 to obtain the final state set { x of the samplet i}i＝1...N。
Effects of the implementation
According to the above steps, target tracking is performed on a plurality of CIF format (352 × 288 pixels) 25fps test sequences, the length of each sequence is different in 200-.
Fig. 4 shows the tracking result of a certain "soccer player" sequence obtained by using the present embodiment, and the target is indicated by a box. Compared with the sequential monte carlo filtering (shown in fig. 2) and the scale-adaptive mean shift (shown in fig. 3) embedded with the mean shift, the method of the embodiment can well track the position change of the target and can more accurately capture the process that the target scale is changed from large to small. Wherein subgraphs (a) - (f) in fig. 2, fig. 3 and fig. 4 depict the position and size changes of the object in time order.
In the computation time analysis of this embodiment as shown in fig. 5, four video sequences were tested by the method of this embodiment (CAMSGPF curve) and the conventional sequential monte carlo filtering method (MSEPF curve) combined with scale-adaptive mean shift. FIG. (a) shows that the average time spent by the method of the present invention on each particle in the "off road vehicle" sequence is 0.138ms, 23.63% faster than the prior art method; graph (b) shows that the method of the present invention spends an average time of 0.043ms per particle in the "ice hockey" sequence, 30.03% faster than the prior art method; FIG. (c) shows that the average time spent by the method of the invention on each particle in the "road-car" sequence is 0.038ms, 45.49% faster than the prior art method; figure (d) shows that the average time the method of the invention spends on each particle in the "football player" sequence is 0.239ms, 34.47% faster than the prior art method. It can be seen that the method (CAMSGPF curve) of the present embodiment can complete the operation task of one frame within 40ms, and the speed is improved by about 30% to 40% compared with the speed of the existing sequential monte carlo filtering method (MSEPF curve) combined with scale adaptive mean shift.
Claims (4)
1. A low-complexity scale self-adaptive video target tracking method is characterized by comprising the following steps:
step one, initializing a particle sample state: determination of the initial state x of an object by detection0Simultaneously calculating the observation vector of the target, giving the number N of the particle sampling points, and setting the initial state vectors of all the sampling points to be x0And are given uniform weights
Step two, in the particle sample in the step one, for each sampling point i: n is a uniformly distributed random variable U-U [0, 1 ], if The scale factor k of the sampling pointiIs taken to be k1Otherwise, take k as2Wherein: k is a radical of1＝1.2，k2Respectively representing the variation characteristics of target scale enlargement and reduction between frames, calculating a second-order autoregressive central point of a sample, and storing the second-order autoregressive central point as a central point of mean shift;
the calculating of the second-order autoregressive central point of the sample specifically comprises the following steps:
let the current sampling point set be { xt i，wt i}i＝1…NFor each sampling point i ═ 1: n calculating the second-order autoregressive center
In the formula, xt iA state vector representing the ith particle at time t;
step three, obtaining a mean shift field with neighborhood consistency according to the second-order autoregressive center of the sampling points in the sample set in the step two;
step four, theEstablishing an importance sampling density function at each sampling point, and combining the probability angle of Monte Carlo sampling with the mean shift method in the third step to obtain the updated state x of the samplet iAnd in state xt iLower update weight wt iAnd then by setting the sampling points { x }t i，wt i}i＝1…NResampling is carried out to obtain discrete estimation { x) of posterior probability distribution of the target final state at the current momentt i，wt i}i＝1…N；
Establishing an importance sampling density function for each sampling point specifically comprises the following steps:
4.1) randomly dividing the set of particles into two groups, wherein the first group accounts for a proportion of alpha and the second group accounts for a proportion of 1-alpha;
4.2) the states of the particles in the first group are transferred according to a second-order autoregressive model, i.e. the state transfer quantity at the previous moment and a random quantity following a Gaussian distribution having the same dimensionality as the states of the particles are added to the current states of the particles, the mean value is zero, and the variance ΣDGiven empirically or learned from known samples;
4.3) when the state of the second group of particles is sampled, the particles are randomly divided into two groups A and B according to equal probability, the sampling process of the particles of each group is respectively carried out according to a Gaussian distribution, but the position components of the Gaussian distribution centers of the two groups are the convergence positions of the particles through a mean shift method, wherein: the scale component of the group A Gaussian distribution center is selected as a scale-up factor k1Optimization scale of time-adaptive scale method, second-order autoregressive model covariance matrix with variance as superposition scale amplification correction quantity
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</math>
Covariance matrix sigma added with uncertainty of analog adaptive scale mean shift methodCAMWhere I is the maximum number of convergence iterations of the mean shift,
is the final zeroth moment cumulative of the ith sample point.
2. The low-complexity scale-adaptive video target tracking method according to claim 1, wherein the mean shift field with neighborhood consistency is obtained according to the second-order autoregressive center of the sampling points in the sample set in the second step, which is specifically as follows:
firstly, judging each sampling point i to be 1: n, if i ═ 1 or
In that
Outside the neighborhood omega, sequentially executing the second, third, fifth and sixth steps, or else, sequentially executing the fourth, fifth and sixth steps; wherein,
respectively being a second-order autoregressive center of the sampling points i and i-1 at the moment t;
② reference state
<math>
<mrow>
<msup>
<mi>x</mi>
<mi>ref</mi>
</msup>
<mo>≡</mo>
<mrow>
<mo>(</mo>
<msup>
<mi>p</mi>
<mi>ref</mi>
</msup>
<mo>,</mo>
<msup>
<mi>s</mi>
<mi>ref</mi>
</msup>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msubsup>
<mover>
<mi>x</mi>
<mo>·</mo>
</mover>
<mi>t</mi>
<mi>i</mi>
</msubsup>
<mo>,</mo>
</mrow>
</math>
Wherein p isref、srefRespectively position and scale sub-states, sref＝{lref，href}，lref，hrefRespectively describe with prefTarget of centreThe length and height of the rectangular profile; zero order moment cumulant m00(xrefN-0) is 1, n-0: i;
③ 1: i, circulation:
first, a mean shift vector M is calculatedr，g(xref)：
Wherein, { pj}j＝1…MIs the reference state xrefPixel coordinate, m (p), covered by a rectangular area of the imagej) Is position pjWhen the observation vector is a color histogram, the probability weight of (2) is determined by xrefThe matching degree of a color interval between a target color histogram corresponding to the state and a target feature template is obtained, g (-) is a kernel function, and r is a factor for normalizing kernel function envelope;
then, the position sub-state of the reference state is updated:
pref＝pref+Mr，g(xref)
calculating the zeroth order moment density of the reference state:
wherein: m00(xref) Is that the target appears in state xrefAnd updating the zero order moment cumulant in the corresponding rectangular region through the zero order moment density:
m00(xref，n)＝m00(xref，n-1)×m00(xref)
respectively assigning the mean value drift vector, the zeroth order moment density and the zeroth order moment cumulant of the ith sampling point directly according to corresponding information of the ith-1 sampling point;
fifthly, obtaining the final zero-order moment cumulant
<math>
<mrow>
<msubsup>
<mover>
<mi>m</mi>
<mo>·</mo>
</mover>
<mn>00</mn>
<mi>i</mi>
</msubsup>
<mo>=</mo>
<msub>
<mi>m</mi>
<mn>00</mn>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mi>ref</mi>
</msup>
<mo>,</mo>
<mi>I</mi>
<mo>)</mo>
</mrow>
</mrow>
</math>
And a new state through mean shift optimization and scale adaptive adjustment:
storing the end position of the mean shift
<math>
<mrow>
<msup>
<mover>
<mi>p</mi>
<mo>·</mo>
</mover>
<mi>i</mi>
</msup>
<mo>=</mo>
<msup>
<mi>p</mi>
<mi>ref</mi>
</msup>
</mrow>
</math>
And final zeroth moment cumulant
3. The low complexity scalable adaptive video target tracking method of claim 1, wherein the state x ist iLower update weight wt iThe method specifically comprises the following steps:
wherein, p (z)t|xt i) Is in a state xt iThe probability of the observation model of the lower target is obtained by adopting the matching degree of the color interval between the color histogram and the target characteristic template; p (x)t i|x0:t-1 i) Is the dynamic model probability of the target, here a second order autoregressive model is used; q (x)t i|x0:t-1 i，zt) The probabilities are sampled for importance.
4. The low complexity scale-adaptive video target tracking method of claim 1, wherein the set of sample points { x } is selectedt i，wt i}i＝1…NResampling is carried out, and specifically: selecting sampling points with large weight in the sample set, normalizing the weight of all the sampling points to obtain discrete estimation { x ] of posterior probability distribution of the final state of the target at the current momentt i，wt i}i＝1…N。
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| CN101551909B (en) * | 2009-04-09 | 2011-02-02 | 上海交通大学 | Tracking method based on kernel and target continuous adaptive distribution characteristics |
| CN101577005A (en) * | 2009-06-12 | 2009-11-11 | 北京中星微电子有限公司 | Target tracking method and device |
| CN101867699B (en) * | 2010-05-25 | 2012-05-30 | 中国科学技术大学 | Real-time tracking method of nonspecific target based on partitioning |
| CN102063727B (en) * | 2011-01-09 | 2012-10-03 | 北京理工大学 | Covariance matching-based active contour tracking method |
| CN102184554B (en) * | 2011-05-28 | 2012-12-26 | 西安电子科技大学 | Infrared target tracking method based on background perception of activation region |
| CN102270347B (en) * | 2011-08-05 | 2013-02-27 | 上海交通大学 | Target detection method based on linear regression model |
| CN103093010B (en) * | 2011-10-31 | 2016-01-27 | 深圳光启高等理工研究院 | The method for designing of metamaterial modular construction body and design system |
| CN103024349B (en) * | 2012-11-13 | 2015-05-13 | 大连民族学院 | MLE (Maximum Likelihood Estimation) video target tracking method based on sparse limitation |
| CN103345258B (en) * | 2013-06-16 | 2016-05-18 | 西安科技大学 | A kind of Soccer robot target tracking method and system |
| CN104700415B (en) * | 2015-03-23 | 2018-04-24 | 华中科技大学 | The choosing method of matching template in a kind of images match tracking |
| CN104766084B (en) * | 2015-04-10 | 2017-12-05 | 南京大学 | A kind of nearly copy image detection method of multiple target matching |
| CN106023243A (en) * | 2016-04-11 | 2016-10-12 | 上海大学 | Scale adaptive moving object tracking method |
| CN109839622B (en) * | 2017-11-29 | 2022-08-12 | 武汉科技大学 | Multi-target tracking method for parallel computing particle probability hypothesis density filtering |
| CN108280845B (en) * | 2017-12-26 | 2022-04-05 | 浙江工业大学 | Scale self-adaptive target tracking method for complex background |
| CN109934856B (en) * | 2019-01-30 | 2022-08-02 | 南京邮电大学 | Comprehensive target tracking method based on ASMS and particle filter |
| CN109978923B (en) * | 2019-04-04 | 2020-12-08 | 杭州电子科技大学 | Target tracking method and system based on double-template scale self-adaptive correlation filtering |
| CN110717930B (en) * | 2019-08-28 | 2024-02-20 | 河南中烟工业有限责任公司 | Mutation moving target tracking method based on extended Wang-Landau Monte Carlo and KCF |
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