CN109658128A - A kind of shops based on yolo and centroid tracking enters shop rate statistical method - Google Patents

A kind of shops based on yolo and centroid tracking enters shop rate statistical method Download PDF

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Publication number
CN109658128A
CN109658128A CN201811375196.7A CN201811375196A CN109658128A CN 109658128 A CN109658128 A CN 109658128A CN 201811375196 A CN201811375196 A CN 201811375196A CN 109658128 A CN109658128 A CN 109658128A
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frame
shops
target
shop
yolo
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宣琦
袁琴
徐东伟
翔云
刘毅
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Zhejiang University of Technology ZJUT
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Zhejiang University of Technology ZJUT
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0203—Market surveys; Market polls
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items

Abstract

A kind of shops based on yolo and centroid tracking enters shop rate statistical method, is related to monitor video, deep learning, computer vision, pedestrian detection field, and the present invention is measured in real time the picture in monitor video by yolo target detection model；To obtain target category target position corresponding with its in every frame image data, the classification for belonging to people is filtered out, the centroid position of the target is stored；Again by centroid tracking algorithm keeps track target, unique distribution of Target id is kept, target direction of motion is calculated by the frame up and down of video, counted into shop and by the number of shops, obtain discrepancy shop rate.The present invention provides a kind of shops based on yolo and centroid tracking and enters shop rate statistical method, and this method algorithm is simple, is conducive to software realization, passes through target detection and centroid tracking algorithm realizes the statistics in pedestrian movement direction.

Description

A kind of shops based on yolo and centroid tracking enters shop rate statistical method
Technical field
The present invention relates to monitor video, deep learning, computer vision, pedestrian detections, are based on more particularly to one kind The shops of yolo and centroid tracking enters shop rate statistical method.
Background technique
Nowadays the state for having a comparison bad, there are many people passed through daily on shop doorway, but into shop just very It is few, however the big volume of the flow of passengers means high price rent, high operation cost, if high cost cannot get corresponding remuneration, undoubtedly It is the beginning that decline is moved towards in shop.To avoid such case, most of shops can attract passenger flow, example by holding activity The activity of such as completely giving, gives a discount at prize drawing, as shop it should be understood that the correct solution which has be to pedestrian's psychology in these activities Read, one camera statistics volume of the flow of passengers be installed and the number that enters shop on shop doorway, so as to obtain which actively activity more by The attention of pedestrian.
The operation cost in shop includes infrastructure and employee compensation, and it is every daily to enter the analysis of shop rate statistical method by shops The volume of the flow of passengers of a period and enter shop rate to distribute employee's number on duty, the waste of human resources can be effectively reduced and reduce operation Cost.
Existing pedestrian counting is broadly divided into three classes, i.e., artificial counting, sensor-based pedestrian counting with based on calculating The pedestrian counting of machine vision.Artificial counting arduously take, for big flow of the people artificial counting will disadvantage it is very much；For Sensor, which carries out pedestrian counting, has limitation, and nowadays infrared ray belongs to a more universal sensor method of counting, to ask Single channel can only be arranged in the precision of detection, be not suitable for a large amount of pedestrian counting of public place；Row based on computer vision People, which counts, can obtain the volume of the flow of passengers by real-time video analysis, have universality and convenience.
For pedestrian detection, in existing method, have using histograms of oriented gradients as the description of pedestrian detection, then Classified with SVM, this method precision is not very high, easy erroneous detection, and the object detection method one- based on deep learning Stage and two-stage precision with higher, but arithmetic speed is slow, is unable to reach the effect of real-time target detection.
Summary of the invention
For achieving the above object, the present invention provides a kind of shops based on yolo and centroid tracking and enters shop rate statistics side Method, this method algorithm is simple, is conducive to software realization, and the system in pedestrian movement direction is realized by target detection and centroid tracking algorithm Meter.
The technical solution adopted by the present invention to solve the technical problems is as follows:
A kind of shops based on yolo and centroid tracking enters shop rate statistical method, comprising the following steps:
1) high angle monitor video is read in, the detection model based on yolov3 detects pedestrian: reads shops doorway monitoring view Frequently, every frame image of input is pre-processed, shops's business hours section is set, load yolov3 model；
2) it enables centroid tracking algorithm: to the pedestrian detected, creating object tracker and track pair moved in the frame As reruning object detector until reaching n-th frame；
3) calculate into shop rate: tracking pedestrians target calculates the direction of motion of pedestrian by the frame-to-frame differences of before and after frames, leads to It crosses actual process shops doorway number and actually enters shop number, calculate into shop rate.
Further, in the step 1), input picture extracts feature by feature extraction network DarkNet53, from three kinds The step of prediction task is carried out on the characteristic spectrum of different scale.
Further, in the step 2), centroid tracking algorithm is enabled, bounding box is passed to the object detected in every frame Coordinate calculates center-of-mass coordinate (X when n-th frame by these bounding box coordinatesn,Yn), and unique id, meter are distributed for them The Euclidean distance between new bounding box and existing object is calculated, centroid tracking algorithm assumes there is minimum Eustachian distance between them Mass center is identical target id.
Further, in the step 3), tracking pedestrians target, by the frame-to-frame differences of the n-th frame of target and the (n-1)th frame, Pedestrian movement direction is calculated, and is actually passed through number according to the shops doorway counted and enters shop number and obtain discrepancy shop rate.
Compared with prior art, it is the advantages of technical solution of the present invention:
(1) the present invention provides a kind of shops based on yolo and centroid tracking to enter shop rate statistical method, solves existing The problem of detection accuracy is not high and detection model is difficult to real-time.
(2) in the environment of the present invention is under complex environment, for example flow of the people is larger and illumination changes, it may have compared with High precision and faster detection speed.
(3) present invention is counted to entering shop rate in real time, can preferably help shops to improve into shop conversion ratio and section About operation cost.
Detailed description of the invention
Fig. 1 is the flow chart that pedestrian enters shop rate statistical method.
Fig. 2 is the network structure of yolov3.
Specific embodiment
The principle and features of the present invention will be described below with reference to the accompanying drawings, and example is only applied to explain the present invention, It is not intended to limit application of the invention.
Referring to Figures 1 and 2, a kind of shops based on yolo and centroid tracking enters shop rate statistical method, comprising the following steps:
1) high angle monitor video is read in, detects pedestrian with the detection model based on yolov3, process is as follows；
1.1, monitor video is read:
Camera on shops doorway provides video data for the present invention and pre-processes every frame image, by point of every frame image Resolution is adjusted to 416*416, and shops's business hours section is arranged, program can be allowed to only focus on and reduce additional calculating in the business hours Resource；Skip-frames is set, and object detection is computationally costly, but it contributes positively to our tracker again The object in frame is assessed, in the present invention, default detector model skips 50 frames between test object.
1.2, the pedestrian in yolov3 real-time detection video
Yolov3 is initialized first, reads Parameter File, parses yolov3 model, stress model weight.It will place The video image data managed is synchronized in GPU video memory, into YOLO network layer handles.The feature extraction network that YOLOv3 is used It is DarkNet53, this network is formed by stacking by residual unit, so that training deep layer network difficulty greatly reduces.Every frame input Image pass through feature extraction network first and extract feature, export the convolution characteristic pattern of S*S, and input picture is divided into S*S Grid cell predicts target category and coordinate using anchor boxes by prediction interval.Bounding box coordinate (bx,by, bw,bh) predictor formula are as follows:
bx=σ (tx)+cx
by=σ (ty)+cy
Wherein tx,ty,tw,thIt is the center point coordinate and prediction frame of each bounding box prediction for yolov3 It is wide, high, cx,cyFor the coordinate shift amount of grid cell grid where the centre coordinate of frame, pw,phFor the width for predicting preceding anchor It is high.
There are three anchor by a grid cell in yolov3, when the center of some object in ground truth Coordinate falls in that grid cell and just predicts the object by the grid cell, calculates b in selectionx,by,bw,bhLoss When, the loss of highest that anchor calculating ground truth of confidence in three anchor is selected, if do not had Target's center's point is fallen in the grid cell, then does not calculate bx,by,bw,bhLoss, yolov3 use more Classification Loss letters Number is two Classification Loss function binary_cross_entropy:
Wherein y(i)For two tag along sort values, i ∈ { 1,2 }, x(i)To predict output valve, it is worth between 0~1.Finally to guarantor The target frame stayed carries out very big inhibition processing, removes repeat block, chooses the highest target frame of target category probability of occurrence, and export Its specific coordinate.
2) centroid tracking algorithm is enabled, keeps the distribution of unique id, process is as follows:
2.1, mass center is calculated by bounding box coordinates
The object each to detect is passed to one group of bounding box coordinates (startX in each framen,startYn,endXn, endYn), by these bounding box coordinates, calculate center-of-mass coordinate (X when n-th framen,Yn), and unique id is distributed for them, Wherein:
Object ID sequence in video frame are as follows:
ID={ id1,id2,...,idj}
Wherein, idjFor a subject object, j ∈ N.
2.2, the Euclidean distance between new bounding box and existing object is calculated
For the (n+1)th frame in video flowing, the step 2.1 of computation object mass center；In order not to destroy to image tracing, I It is firstly the need of determining whether that the mass center of n-th frame is associated with mass center when the (n-1)th frame, rather than for each detection The new unique id of the object distribution arrived.In order to complete this process, we calculate a I target that n-th frame occurs altogether in video The Euclidean distance d between all id mass centers that mass center and the (n-1)th frame occur:
Wherein (Xin, Yin) be n-th frame in i-th of target centroid coordinate, 0≤i≤I, (Xj(n-1),Yj(n-1)) it is n-th- Id in 1 framejCenter-of-mass coordinate, j ∈ N.Centroid tracking algorithm assumes that the mass center between them with minimum Eustachian distance is identical Target id, i.e. id(i)=idj, wherein (i), j meets d(i)j=min { dij}。
2.3, new object is registered
For can not associated target with any existing object mass center, it would be desirable to be registered as new object, be distributed for it New object id, and store the mass center of the bounding box coordinates of the object, then repeatedly 2.2, it is that each frame in video flowing repeats The steps flow chart.
2.4, the old object of cancel register
Object tracking algorithm there is a need for being able to when process object loses, and disappears or leaves the visual field, the invention for us, When old object can not be with existing any 50 successive frames of object matching, we are by cancel register object id.
2.5, traceable object is created
In order to facilitate the target for tracking and calculating in video flowing, traceable target need to be stored, object can be traced Condition are as follows: it is object id, it be before mass center, which is not calculated.After storage mass center is to be able to conveniently The continuous direction of motion for calculating the mass center.
3) it calculates into shop rate, process is as follows:
3.1, determine that shop goes out shop number
The door line ordinate position that video area is arranged is H, it is first determined the direction of motion of mass center, by same target Difference (the Δ X of the center-of-mass coordinate average value of id n-th frame mass center ordinate and all preceding n-1 framesn,ΔYn) determine the fortune of target Dynamic direction, wherein difference calculates are as follows:
Charging to shop state is ' 1 ', and shop state is ' -1 ' out, enters shop to pedestrian and goes out shop behavior differentiation as shown in table 1, other feelings Condition does not make distinguishing rule.
ΔYn Yn Yn-1 State
>0 >H <H 1
<0 <H >H -1
Table 1
3.2, it determines in the number passed through outdoors
In order to accurately determine the volume of the flow of passengers outside shops, counting is divided into two parts, first is that by left side video boundaries line Flow of the people, second is that pass through the right video boundaries line flow of the people, setting left side video boundaries frame abscissa positions be W1, the right Video boundaries frame abscissa is W2。
Note passes through to the right a target, and state is ' R '；The state Jing Guo a target is ' L ' to the left, to pedestrian through moving into one's husband's household upon marriage Shop behavior differentiates as shown in table 2, is the sum of two states number in the number of the total process of shops, other situations do not make to differentiate according to According to.
ΔXn Xn Xn-1 Judgement
> 0 > W2 < W2 R
< 0 < W1 > W1 L
Table 2
3.3, it calculates into shop rate
When there is the people to go out from shops in video monitoring section, can upset outdoors through remarkable counting, so we T moment is accurately calculated using following formula enters shop rate ηt。
Wherein ItAlways to enter shop number, C to t momenttIt is remarkable for the total warp detected in shops two sides to t moment Number, OtAlways to go out shop number to t moment.
Example: a kind of embodiment that the shops based on yolo and centroid tracking enters shop rate statistical method is as follows:
(1) experimental data is chosen
The experimental data that the present invention chooses is the flow of the people video of the laboratory building of oneself acquisition, and Shi Changwei 5min records The flow of the people of the people and disengaging laboratory building that pass through within this hour on experiment doorway.
The present invention needs to pre-process image first using YOLOv3 model, by each frame image of input point Resolution is adjusted to 416*416.Such as attached drawing 2, the feature extraction network that YOLOv3 is used is DarkNet53, this network is by residual error list Member is formed by stacking, so that training deep layer network difficulty greatly reduces, different from YOLO version before, YOLOv3 is from three kinds of differences Prediction task is carried out on the characteristic spectrum of scale.The image of every frame input passes through feature extraction network first and extracts feature, output The convolution characteristic pattern of 13*13,1024 channels, obtain first characteristic spectrum, size by a series of convolution operation altogether Constant, port number is reduced to 75 channels, and primary prediction is done on this characteristic spectrum；By 79 layers of 13*13*512's Feature map carries out one time convolution sum one time * 2 up-sampling, generates the feature map of 26*26*256, will up-sample feature It is connect with 61 layers of convolution feature, the characteristic spectrum of a 26*26*75 is obtained by a series of convolution features, in this feature Second of prediction is done on map；The characteristic spectrum of 91 layers of 26*26*256 of input up-sample for one time convolution one time * 2, The feature map of 52*52*128 is generated, while up-sampling feature being connect with 36 layers of convolution feature, by convolution operation The characteristic spectrum of 52*52*75 size is obtained, third time is done on this characteristic spectrum and is predicted.It is done under Analysis On Multi-scale Features map Prediction, improves the precision of small target deteection.
(2) experimental result
Pedestrian is detected after finishing according to step (1) stress model in embodiment 1, calculates current number and disengaging doorway Number, as a result detect to enter shop rate precision as shown in table 3:
Detection behavior Verification and measurement ratio
Pedestrian 80%
Enter shop 66.7%
Shop out 90%
Enter shop rate 94%
Table 3.

Claims (4)

1. a kind of shops based on yolo and centroid tracking enters shop rate statistical method, which is characterized in that the method includes following Step:
1) high angle monitor video is read in, the detection model based on yolov3 detects pedestrian: reads shops doorway monitor video, it is right Every frame image of input is pre-processed, and shops's business hours section is arranged, and loads yolov3 model；
2) it enables centroid tracking algorithm: to the pedestrian detected, creating object tracker and track the object moved in the frame, directly To n-th frame is reached, object detector is reruned；
3) calculate into shop rate: tracking pedestrians target calculates the direction of motion of pedestrian, passes through reality by the frame-to-frame differences of before and after frames Border by shops doorway number and actually entering shop number, calculate into shop rate.
2. a kind of shops based on yolo and centroid tracking as described in claim 1 enters shop rate statistical method, it is characterised in that: In the step 1), input picture extracts feature by feature extraction network DarkNet53, from the characteristic pattern of three kinds of different scales The step of prediction task is carried out in spectrum.
3. a kind of shops based on yolo and centroid tracking as claimed in claim 1 or 2 enters shop rate statistical method, feature exists In: in the step 2), centroid tracking algorithm is enabled, bounding box coordinates are passed to the object detected in every frame, pass through these Bounding box coordinates calculate center-of-mass coordinate (X when n-th framen,Yn), and distribute unique id for them, calculate new bounding box with Euclidean distance between existing object, centroid tracking algorithm assume that the mass center between them with minimum euclidean distance is identical Target id.
4. a kind of shops based on yolo and centroid tracking as claimed in claim 1 or 2 enters shop rate statistical method, feature exists In in the step 3), tracking pedestrians target calculates pedestrian movement by the n-th frame of target and the frame-to-frame differences of the (n-1)th frame Direction, and be actually passed through number according to the shops doorway counted and enter shop number and obtain discrepancy shop rate.
CN201811375196.7A 2018-11-19 2018-11-19 A kind of shops based on yolo and centroid tracking enters shop rate statistical method Pending CN109658128A (en)

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Cited By (5)

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Publication number Priority date Publication date Assignee Title
CN111008595A (en) * 2019-12-05 2020-04-14 武汉大学 Private car interior rear row baby/pet groveling window distinguishing and car interior atmosphere identifying method
CN111353377A (en) * 2019-12-24 2020-06-30 浙江工业大学 Elevator passenger number detection method based on deep learning
CN111832489A (en) * 2020-07-15 2020-10-27 中国电子科技集团公司第三十八研究所 Subway crowd density estimation method and system based on target detection
CN114785955A (en) * 2022-05-05 2022-07-22 广州新华学院 Motion compensation method, system and storage medium for dynamic camera in complex scene
CN115601606A (en) * 2022-12-15 2023-01-13 苏州万店掌软件技术有限公司（Cn） Store state detection method, device, equipment and storage medium

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Cited By (6)

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Publication number Priority date Publication date Assignee Title
CN111008595A (en) * 2019-12-05 2020-04-14 武汉大学 Private car interior rear row baby/pet groveling window distinguishing and car interior atmosphere identifying method
CN111353377A (en) * 2019-12-24 2020-06-30 浙江工业大学 Elevator passenger number detection method based on deep learning
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