CN101887587B - Multi-target track method based on moving target detection in video monitoring - Google Patents
Multi-target track method based on moving target detection in video monitoring Download PDFInfo
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Abstract
The invention discloses a multi-target track method based on moving target detection in video monitoring. The method comprises the following steps: firstly, using a background removing method to detect foreground moving targets; then establishing an incidence matrix between a foreground target block mass of the current frame and the target detected in a previous frame so as to judge various states of the targets (such as target missing, target keeping initial condition, target overlap, target separation and the like), and secondarily tracking the targets which are in the separation condition; and at last, updating the positions and areas of the targets, kernel weighted color histograms and other characteristics to achieve tracking multiple targets. The method of the invention can be used for effectively and reliably tracking sheltered targets and the multiple targets which are mutually overlapped and then separated in real time, and improving stability and robustness of a video monitoring system.
Description
Technical field
The invention belongs to Flame Image Process and computer vision field, be specifically related to a kind of multi-target detection and tracking in video monitoring system.
Background technology
The detection of target is an important subject of computer vision field with following the tracks of in the video sequence; Be the basis of subsequent treatment such as target classification, behavioural analysis, have important use at numerous areas such as intelligent transportation control, intelligent video monitoring, military guidances and be worth.
In recent years, people have proposed many and method for target following, summarize and get up mainly to be divided into following four types:
(1) based on the tracking in zone: at first set up To Template, according to the template tracking target, its restrictive condition is that target can not be blocked, otherwise lose objects easily;
(2) based on the tracking of characteristic: extract the characteristic informations such as color, barycenter of target, calculate tracking target, generally need to merge multiple information through similarity;
(3) based on the tracking of profile template: the boundary profile of getting target is as template, tracking target profile in the edge image of subsequent frame, but responsive to profile variations;
(4) based on the tracking of model: generally use a precise geometrical model to follow the tracks of, and the timely replacement model parameter, accurate tracking target, but calculate complicated.
Because the space strong correlation that video image exists and the time continuity of target state, so realize that through the coincidence area that calculates the consecutive frame foreground target target following is possible between consecutive frame.As being that the Chinese patent of CN101339608A discloses " a kind of based on the method for tracking target and the system that detect " at publication number; This patent is set up the tracking target formation; And present frame testing result and object queue are mated according to position and yardstick; Realize that target following and state upgrade, strengthened the real-time of following the tracks of.But when target took place to overlap or separates, reliability was low, followed the tracks of unstable.
Summary of the invention
Technical matters: the objective of the invention is to disclose a kind of moving object detection and multi-object tracking method that is applied in the video monitoring system; Realization is carried out in real time, is followed the tracks of reliably a plurality of targets of separating again by the target of partial occlusion and after overlapping, the stability and the robustness of raising video monitoring system.
Technical scheme: the technical scheme that the present invention will solve is: at first use background subtraction to detect the foreground moving target; Set up the incidence matrix between the detected target of present frame foreground target agglomerate and former frame then; And with this judge the residing various states of target (as target disappear, target keeps original state, target coincidence, target separation etc.), the target that is in released state is carried out two secondary trackings; Characteristics such as the position of final updating target, area and nuclear weighting color histogram realize the tracking to a plurality of targets, and concrete steps are following:
Multi-object tracking method based on moving object detection in the video monitoring of the present invention may further comprise the steps:
A, employing background subtraction detect moving target;
The probability distribution of the nuclear weighting color histogram of B, calculating moving target;
C, set up the incidence matrix between present frame foreground target agglomerate and the detected target of former frame;
D, judge and the state of living in of each target the target that is in released state is carried out two secondary trackings;
E, more position, area and the nuclear weighting color histogram characteristic of fresh target.
In the said steps A, adopt the concrete steps of background subtraction detection moving target following:
A1, present frame gray level image and background image are subtracted each other, taking absolute value obtains error image;
A2, use adaptive threshold carry out binary conversion treatment to error image;
A3, to bianry image carry out the computing of form open-close, connected region label, fill up " cavity ", the aftertreatments such as length breadth ratio judgement of connected region area size and minimum boundary rectangle; After the influence that elimination noise and background disturbance bring, obtain foreground target binaryzation mask;
A4, use foreground target binaryzation mask and present frame input picture carry out the logical operation, detect moving target.
Among the said step C, the concrete steps of setting up the incidence matrix between the detected target of present frame foreground target agglomerate and former frame are following:
C1, make Obj_n and Blob_m represent detected n the target of former frame and m foreground target agglomerate of present frame respectively, calculate the area S that overlaps between Blob_m and the Obj_n
Mn, m=1 wherein, 2 ... M, n=1,2 ... N, M, N are respectively present frame foreground target agglomerate number and the detected target number of former frame;
C2, set up the incidence matrix S of the capable N row of M, its element is S
Mn
C3, incidence matrix S is carried out binary conversion treatment, calculate each row of S, nonzero term number Row_m and Col_n of each row then respectively.
Among the said step D,, the residing state of target is divided into five kinds of situation according to the relation that overlaps of the detected target of former frame with present frame foreground target agglomerate:
D1, if Obj_n does not exist with any foreground target agglomerate to be overlapped, promptly Col_n=0 then judges target Obj_n disappearance;
D2, if Obj_n and Blob_m existence overlaps, and be one-to-one relationship, i.e. Col_n=1, S
Mn=1, Row_m=1 judges that then target Obj_n keeps original state;
D3, if Obj_n only exists with Blob_m to be overlapped, but Blob_m also overlaps with other targets existence, i.e. Col_n=1, S
Mn=1, Row_m＞1 judges that then target Obj_n overlaps with other targets;
D4, if Blob_m does not exist with any target of former frame to be overlapped, promptly Row_m=0 then judges the fresh target generation;
D5, if Obj_n overlaps with the existence of a plurality of foreground target agglomerates, promptly Col_n＞1 judges that then target Obj_n is in released state.
Among the said step D5, to being in the target of released state, overlap if Obj_n exists with a plurality of foreground target agglomerates, then bonded area, color characteristic carry out two secondary trackings, and its concrete steps are following:
D51, delete the foreground target agglomerate of its area greater than 1.5 times of Obj_n areas;
D52, the remaining mutual distance of foreground target agglomerate of basis are judged the reason of separating;
D53, if separating reason is the influence that blocked by background, the boundary rectangle of then getting all foreground target agglomerates is as the corresponding foreground area of target;
D54, be to isolate fresh target if separate reason; The probability distribution of the nuclear weighting color histogram of the foreground target agglomerate of calculated candidate then; And calculate the similarity Bhattacharyya coefficient ρ of probability distribution of the nuclear weighting color histogram of itself and former frame target Obj_n respectively, find out ρ and be the current goal zone of peaked foreground target agglomerate Blob_x for being complementary with Obj_n.
In the said step e, each target of former frame that obtains according to incidence matrix S and the matching relationship of present frame foreground target agglomerate, characteristics such as the position of fresh target, area and color more, concrete steps are:
E1, the position that obtains Blob_x, area features, and upgrade position, the area of Obj_n with this;
E2, upgrade the nuclear weighting color histogram of Obj_n with the nuclear weighting color histogram of Blob_x.
Beneficial effect: experimental result shows, the inventive method can be effectively to by the target of partial occlusion and a plurality of targets of separating again after overlapping carry out in real time, follow the tracks of reliably, the stability and the robustness of raising video monitoring system.
Compared with prior art, the invention has the advantages that:
1) target is followed the tracks of adopting background subtraction to detect automatically on the basis of moving target; In the target following process, the residing state of target is divided into fresh target produces, target disappears, target keeps original state, target overlaps with target and separates five kinds of situation, except that the target separation; Other four kinds of situation are utilized consecutive frame zone coupling; Guaranteeing that shortcut calculation has improved real-time under the prerequisite of effectively following the tracks of.
2) when target is in released state, to different separation reasons, characteristics such as the color of combining target, position, area are carried out two secondary trackings.Adopt nuclear weighting color histogram to describe the colouring information of target, compare general color histogram, can effectively reduce the interference of background and other separate targets, improve the reliability of following the tracks of.
Description of drawings
Fig. 1 is the multi-object tracking method process flow diagram based on moving object detection of the present invention.
Embodiment
Realization based on the multi-object tracking method of moving object detection in the video monitoring of the present invention mainly comprises following steps:
Step 1: adopt background subtraction to detect moving target
(1) present frame gray level image and background image are subtracted each other, taking absolute value obtains error image;
(2) use adaptive threshold, error image is carried out binary conversion treatment;
(3) to bianry image carry out the computing of form open-close, connected region label, fill up " cavity ", the aftertreatments such as length breadth ratio judgement of connected region area size and minimum boundary rectangle; After the influence that elimination noise and background disturbance bring, obtain foreground target binaryzation mask;
(4) use foreground target binaryzation mask and present frame input picture to carry out the logical operation, detect moving target.
Step 2: the probability distribution of calculating the nuclear weighting color histogram of moving target
Color histogram is a kind of effective sign clarification of objective, because can accurately reflect color of object information, do not change, be easy to characteristics such as calculating with target shape, in target following, is used widely.
Suppose that R is the rectangular area track window that comprises target, it is centered close to x
0, the probability distribution p={p (u) of the nuclear weighting color histogram of R then }
U=1 ..., KCan calculate by formula (1).
In the formula (1): C is a normalization coefficient, makes
Wherein K is the color characteristic space dimensionality, gets K=8 * 8 * 8=512; L representes the number of pixels in the rectangular area; x
iFor in the zone more arbitrarily, || x
i-x
0|| be x
iTo x
0Distance; H is the kernel function bandwidth, gets and follows the tracks of the half the of window width; B (x
i) be x
iThe color range of pairing color space is to pixel x
iCorresponding color characteristic carries out the K level and quantizes; δ [] is a unit impulse function, as pixel x
iWhen corresponding color characteristic falls into u color characteristic, δ [b (x
i)-u] value be 1; K () is a kernel function, and it is defined as:
Can find out that from the expression formula of kernel function the area contribution that is positioned at target central authorities is maximum, and the marginarium is blocked or it is bigger to receive the possibility of background influence, so contribution is minimum.Can find out that the nuclear weights reduce comparatively slowly, therefore have more zone to obtain bigger weights, this is very favourable to improving tracking stability.
Step 3: set up the incidence matrix between the detected target of present frame foreground target agglomerate and former frame
(1) makes Obj_n and Blob_m represent detected n the target of former frame and m foreground target agglomerate of present frame respectively, calculate the area S that overlaps between Blob_m and the Obj_n
Mn:
S
mn＝A(Obj_n)∩A(Blob_m) (2)
M={1 wherein, 2 ... M}; N={1,2 ... N}, M, N are respectively present frame foreground target agglomerate number and the detected target number of former frame, and the boundary rectangle area of target is extracted in A () expression, and ∩ representes to ask the coincidence area;
(2) set up the incidence matrix S that a capable N of M is listed as, its element is S
Mn(m=1,2 ... M; N=1,2 ... N);
(3) binaryzation incidence matrix S, order
In the formula (4), α is a coefficient, gets α=0.3; Min () representes to minimize function; S
Mn=1 expression Blob_m effectively overlaps with the Obj_n existence, otherwise S
Mn=0.
(4) each row of compute associations matrix S, the nonzero term number Row_m and the Col_n of each row:
Step 4: judge the residing state of each target, the target that is in released state is carried out two secondary trackings according to the overlap relation of the detected target of former frame with present frame foreground target agglomerate, judge the residing state of each target, concrete steps are:
(1) if Obj_n does not exist with any foreground target agglomerate to be overlapped, promptly Col_n=0 judges that then target Obj_n disappears;
(2), and be one-to-one relationship if Obj_n and Blob_m existence overlaps, i.e. Col_n=1, S
Mn=1, Row_m=1 judges that then target Obj_n keeps original state;
(3) if Obj_n only exists with Blob_m to be overlapped, but Blob_m also overlaps with other targets existence, i.e. Col_n=1, S
Mn=1, Row_m＞1 judges that then target Obj_n overlaps with other targets;
(4) if Blob_m does not exist with any target of former frame to be overlapped, promptly Row_m=0 judges that then fresh target produces;
(5) if existing with J foreground target agglomerate, Obj_n overlaps; Be Col_n=J; And J＞1 judges that then target Obj_n is in released state, and then characteristic such as bonded area, nuclear weighting color histogram is carried out two secondary trackings; The task of two secondary trackings is exactly therefrom accurately to find out one or several foreground target agglomerate of corresponding target Obj_n, and its concrete steps are following:
1. delete the foreground target agglomerate of its area, that is: if A (Blob_m)＞1.5A (Obj_n) then makes S greater than 1.5 times of Obj_n areas
Mn=0;
2. ask for the maximal value in each foreground target agglomerate and other foreground target agglomerate minimum spacing, that is: d
Max=max [min (d (Blob_i, Blob_j))];
3. if d
Max≤T, (T is a distance threshold, gets T=10) then is judged to and blocked by background and target divides, and the boundary rectangle of getting all foreground target agglomerates is as the corresponding foreground area of target, more fresh target;
4. if d
Max＞T then is judged in the former target and divides fresh target, by the probability distribution q={q (u) of the nuclear weighting color histogram of the foreground target agglomerate of formula (1) calculated candidate }
U=1 ..., KAnd calculate the similarity Bhattacharyya coefficient ρ of probability distribution p (u) of the nuclear weighting color histogram of q (u) and former frame target Obj_n respectively by formula (7), find out ρ and be the current goal zone of peaked foreground target agglomerate Blob_x for being complementary with Obj_n;
Step 5: each target of former frame that obtains according to incidence matrix S and the matching relationship of present frame foreground target agglomerate, characteristics such as the position of fresh target, area and color more, concrete steps are:
(1) obtains position, the area features of Blob_x, and upgrade position, the area of Obj_n with this;
(2) upgrade the nuclear weighting color histogram of Obj_n with the nuclear weighting color histogram of Blob_x.
Claims (2)
- In the video monitoring based on the multi-object tracking method of moving object detection, it is characterized in that this method may further comprise the steps:A, employing background subtraction detect moving target;The probability distribution of the nuclear weighting color histogram of B, calculating moving target;C, set up the incidence matrix between present frame foreground target agglomerate and the detected target of former frame;D, judge and the state of living in of each target the target that is in released state is carried out two secondary trackings;E, more position, area and the nuclear weighting color histogram characteristic of fresh target;In the said steps A, adopt the concrete steps of background subtraction detection moving target following:A1, present frame gray level image and background image are subtracted each other, taking absolute value obtains error image;A2, use adaptive threshold carry out binary conversion treatment to error image;A3, to bianry image carry out the computing of form open-close, connected region label, fill up " cavity ", the length breadth ratio of connected region area size and minimum boundary rectangle judges aftertreatment; After the influence that elimination noise and background disturbance bring, obtain foreground target binaryzation mask;A4, use foreground target binaryzation mask and present frame input picture carry out the logical operation, detect moving target;Among the said step C, the concrete steps of setting up the incidence matrix between the detected target of present frame foreground target agglomerate and former frame are following:C1, make Obj_n and Blob_m represent detected n the target of former frame and m foreground target agglomerate of present frame respectively, calculate the area S that overlaps between Blob_m and the Obj_n Mn, m=1 wherein, 2 ... M, n=1,2 ... N, M, N are respectively present frame foreground target agglomerate number and the detected target number of former frame;C2, set up the incidence matrix S of the capable N row of M, its element is S MnC3, incidence matrix S is carried out binary conversion treatment, calculate each row of S, nonzero term number Row_m and Col_n of each row then respectively;Among the said step D,, the residing state of target is divided into five kinds of situation according to the relation that overlaps of the detected target of former frame with present frame foreground target agglomerate:D1, if Obj_n does not exist with any foreground target agglomerate to be overlapped, promptly Col_n=0 then judges target Obj_n disappearance;D2, if Obj_n and Blob_m existence overlaps, and be one-to-one relationship, i.e. Col_n=1, S Mn=1, Row_m=1 judges that then target Obj_n keeps original state;D3, if Obj_n only exists with Blob_m to be overlapped, but Blob_m also overlaps with other targets existence, i.e. Col_n=1, S Mn=1, Row_m＞1 judges that then target Obj_n overlaps with other targets;D4, if Blob_m does not exist with any target of former frame to be overlapped, promptly Row_m=0 then judges the fresh target generation;D5, if Obj_n overlaps with the existence of a plurality of foreground target agglomerates, promptly Col_n＞1 judges that then target Obj_n is in released state;In the said step e, each target of former frame that obtains according to incidence matrix S and the matching relationship of present frame foreground target agglomerate, the more position of fresh target, area and color characteristic, concrete steps are:E1, the position that obtains foreground target agglomerate Blob_x, area features, and upgrade position, the area of Obj_n with this;E2, upgrade the nuclear weighting color histogram of Obj_n with the nuclear weighting color histogram of Blob_x.
- 2. in the video monitoring according to claim 1 based on the multi-object tracking method of moving object detection; It is characterized in that; Among the said step D5,,, Obj_n overlaps if existing with a plurality of foreground target agglomerates to being in the target of released state; Then bonded area, color characteristic carry out two secondary trackings, and its concrete steps are following:D51, delete the foreground target agglomerate of its area greater than 1.5 times of Obj_n areas;D52, the remaining mutual distance of foreground target agglomerate of basis are judged the reason of separating;D53, if separating reason is the influence that blocked by background, the boundary rectangle of then getting all foreground target agglomerates is as the corresponding foreground area of target;D54, be to isolate fresh target if separate reason; The probability distribution of the nuclear weighting color histogram of the foreground target agglomerate of calculated candidate then; And calculate the similarity Bhattacharyya coefficient ρ of probability distribution of the nuclear weighting color histogram of itself and former frame target Obj_n respectively, find out ρ and be the current goal zone of peaked foreground target agglomerate Blob_x for being complementary with Obj_n.
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