CN109697420A - A kind of Moving target detection and tracking towards urban transportation - Google Patents
A kind of Moving target detection and tracking towards urban transportation Download PDFInfo
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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- 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
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Abstract
The invention belongs to intelligent transportation fields, more particularly to a kind of Moving target detection and tracking towards urban transportation, by setting up camera in urban road and intersection, with camera to including motor vehicle, the traffic scene of non-motor vehicle and pedestrian target is shot, obtain video data, and utilize the characteristics of image of video data, a kind of Model Matching algorithm is proposed based on target detection and is combined this algorithm and KCF algorithm to have obtained a kind of multiple target tracking algorithm, target trajectory is obtained using obtained multiple target tracking algorithm and the complete trajectory of target is stored, reach and the target in range of video is detected automatically, classification and lasting tracking, obtain the purpose of the motion track information of target.The method of the present invention can accurately obtain the type and its motion track information of different target using continuous image.
Description
Technical field
The invention belongs to intelligent transportation fields, and in particular to a kind of Moving target detection and track side towards urban transportation
Method.
Background technique
With intelligent traffic monitoring system widely popularize and the fast development of computer vision technique, based on video
Multi-target detection, classification and tracking are increasingly becoming a key areas of current traffic surveillance and control system research.For road traffic
Target detection scheme in video, the object detection method having at present have frame differential method, background subtraction method and optical flow method etc., but
It is that existing these types of object detection method efficiency is relatively low, was had also been proposed later based on RCNN, fastRCNN, fasterRCNN
With the object detection method of RFCN, and YOLO V3 algorithm has been evolved on this basis, in YOLO V3 algorithm, technical staff
Using multi-scale prediction and a better base categories network is improved, faster, false detection rate is low, versatility for detection speed
By force.
For the tracking scheme of target in road traffic video, traditional method for tracking target be generate class method with
Track, that is, the target of prediction next frame, but there are precision is not high and rate is slower, thus popular be differentiate class with
The method of track is mainly based upon the tracking of testing result, herein for tracking, proposes two solutions: (1) to video sequence
Each frame of column carries out moving object detection, based on the detection and result completion movement objective orbit connection between consecutive frame, obtains
Take moving target information.(2) target is detected in initial frame and is effectively described, then searched in subsequent image sequence
With the target area having detected target and matching, final tracking obtains movement objective orbit.But there is detection in the first scheme
Effect is unstable, there are problems that target missing inspection leads to tracking failure.Second scheme avoids the first scheme dependent on inspection
The disadvantage that measuring tape comes, the thinking for having used one-time detection repeatedly to track, core are the extraction and description of target signature.But feature
Point, which extracts, excessively causes matching difficult, and characteristic point is very few and be easy to cause erroneous detection, and feature point extraction process calculates complicated, consumption
Duration.
Another way is to its feature of target whole description, and commonly used target signature includes image border, shape, line
Reason, statistical color histogram feature etc. enhance the reliability of target signature by merging multiple features.Feature is carried out to target to mention
After taking, target reorientation is carried out using similarity measurement mode, realizes target following.The common tracking based on characteristic matching
Algorithm has the matched tracking of target image based on binaryzation, based on Edge Feature Matching, based on color of object characteristic matching
Tracking etc..But its, noise fuzzy for image etc. is more sensitive, the extraction effect of feature also rely on various extraction operators and its
The setting of parameter, in addition, the more difficult determination of continuous interframe feature corresponding relationship, has negative influence to tracking performance.
Summary of the invention
Aiming at the problem that precision target detection existing in the prior art and tracking is low and is difficult to adapt to complex scene, this
Invention proposes a kind of Moving target detection and tracking towards urban transportation, comprising the following steps:
Step 1: acquiring the video of traffic scene, obtain video interception, classification annotation is carried out to video interception, after mark
Video interception as sample set；
Step 2: the sample set that step 1 obtains being trained using YOLO V3 algorithm, detection model is obtained, by traffic
The video input detection model of scene obtains the testing result information of the Pixel Information and target of image in each frame, wherein view
The t frame of frequency is expressed as Framet, t expression frame number value is positive integer；
Step 3: creating interim trajectory lists Ts, Ts is sky at this time, the video for the traffic scene that read step 2 obtains
Frame1As present frame, to Frame1In each target for detecting establish new track, and Ts are added in all new tracks, more
New Frame2As present frame, by Frame1In each target testing result information as present frame Frame2Corresponding rail
Mark endpoint information, enters step 4；
Step 4: setting present frame as Framet, then next frame is Framet+1, by FrametIn every final on trajectory information with
FrametThe testing result information of target matched: by FrametThe testing result information conduct of the target of middle successful match
Framet+1In corresponding final on trajectory information, continue track；By FrametThe inspection of the middle object detection results target that it fails to match
Starting point of the result information as new track is surveyed, new track is created and is added in Ts, at this time FrametIn the starting point of new track be
Framet+1Final on trajectory information；By FrametThe target exploitation KCF algorithm of middle final on trajectory information matches failure obtains
FrametMiddle target is corresponding in Framet+1The predicted position information of middle corresponding target continues track, and by track confidence level
Timer+1；Work as FrametWhen not being the last frame of video, Frame is updatedt+1Step 4 is executed as present frame, is otherwise executed
Step 5；
Step 5: the track in Ts being screened, complete trajectory list TA is obtained.
Further, step 1 includes following sub-step:
Step 1.1: acquire the video of traffic scene, obtain 5000 comprising bus, truck, car, motorcycle, from
The video interception of the sample image of the targets such as driving, pedestrian；
Step 1.2: video interception being marked using image labeling tool, the mark includes carrying out to the target in image
Target position in target category and image is labeled, and the video interception after mark is as sample set.
Further, step 2 includes following sub-step:
The sample set that step 1 obtains is trained using YOLOV3 algorithm, obtains detection model, by the view of traffic scene
Frequency input detection model, obtains the testing result information of the Pixel Information and target of image in each frame, wherein the t of video
Frame is expressed as Framet, t expression frame number value is positive integer, ItIndicate the Pixel Information of the image of t frame, the ItIncluding picture
Width, height and area and Pixel Information, DBtIndicate the testing result of t frame, and DBt={ BBi, i=1,2 ..., n },
Wherein BBiIt indicates that t frame detects i-th of target information, obtains the testing result information of target in each frame, the inspection of the target
Surveying result information includes the midpoint coordinates of target detection envelope frame, width, height, the area of target detection envelope frame.
Further, matched process in step 4 are as follows: calculate every track TiEndpoint information BlastWith it is right in present frame
Answer the testing result information BB of targetiDuplication Overlap, Duplication BlastAnd BBiCorresponding two rectangle frames overlapping
Then the ratio of the area in region and total occupied area calculates the pixel distance Dis of the central point of two boundary rectangle frames, finally
B is calculated by the weighted results of Overlap and DislastAnd BBiIt is considered as the matching degree MatchValue of the same target, if
Matching degree is more than or equal to threshold value then successful match, and otherwise it fails to match, and the value range of the MatchValue is [0,1].
Further, MatchValue described in step 4 is set as 0.7.
Further, final on trajectory information matches fail in step 4, obtain Frame using KCF algorithmtMiddle target is corresponding
In Framet+1The predicted position information of middle corresponding target includes following two situation:
If obtaining Framet+1The predicted position information update of existing target is then by the predicted position information of middle target
Framet+1Middle final on trajectory information continues track, and by Timer+1；
If not obtaining Framet+1The predicted position information of middle target, then replicate FrametFinal on trajectory information conduct
Framet+1Final on trajectory information, continue track, and by Timer+1.
Further, step 5 includes following sub-step:
Step 5: the track in Ts being screened, the screening conditions are as follows: when Timer > 30 or track of selected track
When the midpoint coordinates of the target detection envelope frame of endpoint information is located at video boundaries, by selected track from interim trajectory lists Ts
It deletes, and selected track is saved in complete trajectory list TA, obtain complete trajectory list TA.
The present invention have it is following the utility model has the advantages that
The present invention is compared to other methods, possesses more in the extracting the traffic target motion profile in video of the task
High precision and better adaptability, proposes the tracking strategy based on matching degree and characteristic point, has been obviously improved the essence of tracking
Degree.
Detailed description of the invention
Fig. 1 is that target detection tracking result track shows image；
Fig. 2 is traffic scene sample image；
Fig. 3 is that sample marks example image；
Fig. 4 is that deep learning training process loses curve image；
Fig. 5 is deep learning detection result image.
Specific embodiment
The following provides a specific embodiment of the present invention, it should be noted that the invention is not limited to implement in detail below
Example, all equivalent transformations made on the basis of the technical solutions of the present application each fall within protection scope of the present invention.
A kind of Moving target detection and tracking towards urban transportation, includes the following steps:
Step 1: acquiring the video of traffic scene, obtain video interception, classification annotation is carried out to video interception, after mark
Video interception as sample set；
Step 2: the sample set that step 1 obtains being trained using YOLO V3 algorithm, detection model is obtained, by traffic
The video input detection model of scene obtains the testing result information of the Pixel Information and target of image in each frame, wherein view
The t frame of frequency is expressed as Framet, t expression frame number value is positive integer；
Step 3: creating interim trajectory lists Ts, Ts is sky at this time, the video for the traffic scene that read step 2 obtains
Frame1As present frame, to Frame1In each target for detecting establish new track, and Ts are added in all new tracks, more
New Frame2As present frame, by Frame1In each target testing result information as present frame Frame2Corresponding rail
Mark endpoint information, enters step 4；
Step 4: setting present frame as Framet, then next frame is Framet+1, by FrametIn every final on trajectory information with
FrametThe testing result information of target matched: by FrametThe testing result information conduct of the target of middle successful match
Framet+1In corresponding final on trajectory information, continue track；By FrametThe inspection of the middle object detection results target that it fails to match
Starting point of the result information as new track is surveyed, new track is created and is added in Ts, at this time FrametIn the starting point of new track be
Framet+1Final on trajectory information；By FrametThe target exploitation KCF algorithm of middle final on trajectory information matches failure obtains
FrametMiddle target is corresponding in Framet+1The predicted position information of middle corresponding target continues track, and by track confidence level
Timer+1；Work as FrametWhen not being the last frame of video, Frame is updatedt+1Step 4 is executed as present frame, is otherwise executed
Step 5；
Step 5: the track in Ts being screened, complete trajectory list TA is obtained.
The present invention is compared to other methods, possesses more in the extracting the traffic target motion profile in video of the task
High precision and better adaptability, proposes the tracking strategy based on matching degree and characteristic point, has been obviously improved the essence of tracking
Degree.
Specifically, step 1 includes following sub-step:
Step 1.1: as shown in Figures 2 and 3, acquire the video of traffic scene, obtain 5000 comprising bus, truck,
The video interception of the sample image of the targets such as car, motorcycle, bicycle, pedestrian；
Step 1.2: video interception being marked using image labeling tool, mark includes carrying out target to the target in image
Target position in classification and image is labeled, and the video interception after mark is as sample set.
Preferably, the video interception after mark is scaled to the size of 720 × 480 sizes, facilitates processing.
Specifically, step 2 includes following sub-step:
As shown in Figure 4 and shown in Fig. 5, the sample set that step 1 obtains is trained using YOLOV3 algorithm, is detected
The video input detection model of traffic scene is obtained the testing result of the Pixel Information and target of image in each frame by model
Information, wherein the t frame of video is expressed as Framet, t expression frame number value is positive integer, ItIndicate the pixel of the image of t frame
Information, the ItWidth, height and area and Pixel Information including picture, provide basis, DB for clarification of objectivet
Indicate the testing result of t frame, and DBt={ BBi, i=1,2 ..., n }, wherein BBiIndicate that t frame detects i-th of target information,
The testing result information of target in each frame is obtained, the testing result information of the target includes, in target detection envelope frame
Point coordinate (Centx, Centy), width, height, the area of target detection envelope frame；
DBtIt can be sky, representative does not detect target in current image frame.
Finally we are by ItWith DBtIt binds to FrametResult as detection-phase exports, and continues to locate for follow-up phase
Reason, obtains detection model.
Specifically, matched process in step 4 are as follows: calculate every track TiEndpoint information BlastIt is corresponded to in present frame
The testing result information BB of targetiDuplication Overlap, Duplication BlastAnd BBiTwo corresponding rectangle frame overlay regions
Then the ratio of the area in domain and total occupied area calculates the pixel distance Dis of the central point of two boundary rectangle frames, finally leads to
The weighted results for crossing Overlap and Dis calculate BlastAnd BBiIt is considered as the matching degree MatchValue of the same target, if
It is more than or equal to threshold value then successful match with degree, otherwise it fails to match, and the value range of the MatchValue is [0,1].
Preferably, the threshold value of MatchValue is set as 0.7 in step 4.
Specifically, final on trajectory information matches fail in step 4, Frame is obtained using KCF algorithmtMiddle target corresponds to
Framet+1The predicted position information of middle corresponding target includes following two situation:
If obtaining Framet+1The predicted position information update of existing target is then by the predicted position information of middle target
Framet+1Middle final on trajectory information continues track, and by Timer+1；
If not obtaining Framet+1The predicted position information of middle target, then replicate FrametFinal on trajectory information conduct
Framet+1Final on trajectory information, continue track, and by Timer+1.
Specifically, step 5 includes following sub-step:
Step 5: the track in Ts being screened, screening conditions are as follows: Timer > 30 or final on trajectory when selected track
When the midpoint coordinates of the target detection envelope frame of information is located at video boundaries, selected track is deleted from interim trajectory lists Ts
It removes, and selected track is saved in complete trajectory list TA, obtain complete trajectory list TA.
Track of vehicle is obtained as shown in Figure 1.
Claims (7)
1. a kind of Moving target detection and tracking towards urban transportation, includes the following steps:
Step 1: acquiring the video of traffic scene, obtain video interception, classification annotation is carried out to video interception, the view after mark
Frequency screenshot is as sample set；
Step 2: the sample set that step 1 obtains being trained using YOLO V3 algorithm, detection model is obtained, by traffic scene
Video input detection model, obtain the testing result information of the Pixel Information and target of image in each frame, wherein video
T frame is expressed as Framet, t expression frame number value is positive integer；
It is characterized in that, further including following steps:
Step 3: creating interim trajectory lists Ts, Ts is sky, the Frame of the video for the traffic scene that read step 2 obtains at this time1
As present frame, to Frame1In each target for detecting establish new track, and Ts are added in all new tracks, updated
Frame2As present frame, by Frame1In each target testing result information as present frame Frame2Corresponding track
Endpoint information enters step 4；
Step 4: setting present frame as Framet, then next frame is Framet+1, by FrametIn every final on trajectory information with
FrametThe testing result information of target matched: by FrametThe testing result information conduct of the target of middle successful match
Framet+1In corresponding final on trajectory information, continue track；By FrametThe inspection of the middle object detection results target that it fails to match
Starting point of the result information as new track is surveyed, new track is created and is added in Ts, at this time FrametIn the starting point of new track be
Framet+1Final on trajectory information；By FrametThe target exploitation KCF algorithm of middle final on trajectory information matches failure obtains
FrametMiddle target is corresponding in Framet+1The predicted position information of middle corresponding target continues track, and by track suspicious degree
Timer+1；Work as FrametWhen not being the last frame of video, Frame is updatedt+1Step 4 is executed as present frame, is otherwise executed
Step 5；
Step 5: the track in Ts being screened, complete trajectory list TA is obtained.
2. Moving target detection and tracking towards urban transportation as described in claim 1, which is characterized in that step 1
Including following sub-step:
Step 1.1: acquiring the video of traffic scene, obtain 5000 and include bus, truck, car, motorcycle, voluntarily
The video interception of the sample image of the targets such as vehicle, pedestrian；
Step 1.2: video interception being marked using image labeling tool, the mark includes carrying out target to the target in image
Target position in classification and image is labeled, and the video interception after mark is as sample set.
3. Moving target detection and tracking towards urban transportation as described in claim 1, which is characterized in that step 2
Including following sub-step:
The sample set that step 1 obtains is trained using YOLOV3 algorithm, obtains detection model, the video of traffic scene is defeated
Enter detection model, obtain the testing result information of the Pixel Information and target of image in each frame, wherein the t frame table of video
It is shown as Framet, t expression frame number value is positive integer, ItIndicate the Pixel Information of the image of t frame, the ItWidth including picture
Degree, height and area and Pixel Information, DBtIndicate the testing result of t frame, and DBt={ BBi, i=1,2 ..., n }, wherein
BBiIt indicates that t frame detects i-th of target information, obtains the testing result information of target in each frame, the detection knot of the target
Fruit information includes the midpoint coordinates of target detection envelope frame, width, height, the area of target detection envelope frame.
4. Moving target detection and tracking towards urban transportation as described in claim 1, which is characterized in that step 4
In matched process are as follows: calculate every track TiEndpoint information BlastWith the testing result information BB for corresponding to target in present framei
Duplication Overlap, Duplication BlastAnd BBiThe area and total occupied area of two corresponding rectangle frame overlapping regions
Ratio, then calculate two boundary rectangle frames central point pixel distance Dis, finally by the weighting of Overlap and Dis
As a result B is calculatedlastAnd BBiIt is considered as the matching degree MatchValue of the same target, if matching degree more than or equal to if threshold value
With success, otherwise it fails to match, and the value range of the MatchValue is [0,1].
5. Moving target detection and tracking towards urban transportation as claimed in claim 4, which is characterized in that step 4
Described in the threshold value of MatchValue be set as 0.7.
6. Moving target detection and tracking towards urban transportation as described in claim 1, which is characterized in that step 4
Middle final on trajectory information matches failure, obtains Frame using KCF algorithmtMiddle target is corresponding in Framet+1Middle corresponding target it is pre-
Surveying location information includes following two situation:
If obtaining Framet+1The predicted position information update of existing target is then by the predicted position information of middle target
Framet+1Middle final on trajectory information continues track, and by Timer+1；
If not obtaining Framet+1The predicted position information of middle target, then replicate FrametFinal on trajectory information as Framet+1
Final on trajectory information, continue track, and by Timer+1.
7. Moving target detection and tracking towards urban transportation as described in claim 1, which is characterized in that step 5
Including following sub-step:
Step 5: the track in Ts being screened, the screening conditions are as follows: Timer > 30 or final on trajectory when selected track
When the midpoint coordinates of the target detection envelope frame of information is located at video boundaries, selected track is deleted from interim trajectory lists Ts
It removes, and selected track is saved in complete trajectory list TA, obtain complete trajectory list TA.
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| CN111354023A (en) * | 2020-03-09 | 2020-06-30 | 中振同辂(江苏)机器人有限公司 | Camera-based visual multi-target tracking method |
| CN111899275A (en) * | 2020-08-12 | 2020-11-06 | 中国科学院长春光学精密机械与物理研究所 | Target detection tracking method, device and storage medium |
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