CN113963375A - Multi-feature matching multi-target tracking method for fast skating athletes based on regions - Google Patents
Multi-feature matching multi-target tracking method for fast skating athletes based on regions Download PDFInfo
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Abstract
The invention combines deep learning and computer vision algorithm, and particularly discloses a multi-target tracking method for a multi-feature matching speed skating player based on a region, which comprises the following steps: s1, dividing the current area according to the speed skating site; s2, detecting the information of the fast skaters in the picture according to a target detection algorithm; s3, performing zone division on the speed skating player information detected in s2 according to the divided zones in s 1; s4, matching the speed skiers in each area by using the multi-feature information; and s5, matching the areas according to the tracking result of the speed skating players in each area to form a final tracking result. The method of the invention aims at the characteristic information of the speed skating player such as the match duration, the speed skating field and the like, and carries out the multi-target tracking of the speed skating player more robustly through the modes of region segmentation, multi-characteristic matching and the like.
Description
Technical Field
The invention combines deep learning and a computer vision algorithm, and particularly discloses a multi-target tracking method for a multi-feature matching speed skating player based on a region.
Background
The multi-target tracking is a key technology in the field of computer vision, and is widely applied in directions of automatic driving, intelligent monitoring, behavior recognition and the like. However, due to the complexity of the multi-target tracking task, the multi-target tracking task faces more challenges than the target detection and single-target tracking tasks, such as target overlapping, appearance sharp change, appearance similarity and the like. How to solve the problems more effectively has great significance for the application of multi-target tracking technology, so that in the past decades, people propose a wide range of solutions.
Multi Object Tracking (MOT) refers to detecting a plurality of objects such as pedestrians, automobiles, and animals in a video and giving IDs to perform trajectory Tracking without knowing the number of objects in advance. The main task is to locate a plurality of interested targets in a given video at the same time, and different targets have different IDs so as to realize subsequent track prediction, accurate search and other work. Compared with other computer vision tasks, the multi-target tracking task mainly has the following research difficulties: 1) target detection is not accurate enough; 2) frequent target occlusion; 3) the target number is uncertain; 4) similar appearance, interaction between multiple targets.
Nowadays, the multi-target tracking methods with the highest attention in the industry are mainly SORT and deep SORT. The two methods realize multi-target tracking through target detection, Hungary matching and Kalman prediction and updating. However, when the application is landed, various numerical values of the tracking target in a real scene, including the moving distance and moving speed of the tracking target per second, need to be obtained.
In the invention, a multi-feature matching multi-target tracking method for a speed skating player based on a region is provided. The tracking matching for skaters is quite different from the matching methods of existing data sets. In data sets for pedestrians such as the conventional data sets MOT17 and MOT19, most pedestrians perform linear motion, and the tracking time is relatively short. The fast-skating players have long competition time and move according to the field, so the sliding track is relatively fixed, and the existing algorithm can not be used for good matching. Based on the problems, the whole field is tracked regionally, so that the motion states of the speed skating players in each region are relatively consistent; meanwhile, the process of the previous frame and the next frame is better matched in a multi-feature mode. The track of the speed skating player can be better tracked through the multi-zone and multi-characteristic mode.
According to the invention, a multi-target tracking scheme is established by combining deep learning and computer vision methods, and a multi-target tracking method based on multi-region and multi-feature matching is carried out under a single camera, so that the method can be closer to the motion features of the fast-skating players, and the fast-skating players can be better tracked.
Disclosure of Invention
The invention aims to provide a multi-feature matching multi-target tracking method for a speed skating player based on a region, which adopts the following scheme:
a multi-target tracking method for a multi-feature matching speed skating player based on a region comprises the following steps:
s1, dividing the current area according to the speed skating site;
s2, detecting the information of the fast skaters in the picture according to a target detection algorithm;
s3, performing zone division on the speed skating player information detected in s2 according to the divided zones in s 1;
s4, matching the speed skiers in each area by using the multi-feature information;
and s5, matching the areas according to the tracking result of the speed skating players in each area to form a final tracking result.
Further, in the step s1, the current area is divided according to the characteristics of the speed skating field, so that the movement characteristics in each area are relatively fixed;
further, in the step s2, feature information such as the current position and posture of the fast-skating player in the picture is detected according to a target detection algorithm;
further, the following characteristics of the skater are detected:
s21, skater's location and bounding box information;
s22, characteristic dimension vector of speed skating player, for using measurement method to match ID of front and back frames;
s23 posture characteristics of the skater, comprising 17 joints representing the position information of the joints of the skater.
Further, in the above step s3, the speed skating player information detected in s2 is divided into zones according to the zones in s1, so that different tracking models can be applied to each zone;
further, in step s4, according to the information of the skater in each area, the information of the multiple characteristics is used for carrying out association matching on the frame before and after the skater;
further, in step s5, according to the tracking result of the skater in each area, matching between areas to form a final tracking result;
further, the matching process between the regions is as follows:
s51, obtaining the tracking result of the skater in the common part between the areas;
s52, obtaining the matching relation between the IDs in the front and rear regions in the time period through voting, matching the regions by using the tracking result of the common part to obtain the final matching result, and finally forming a complete tracking track.
The invention has the following advantages:
the method uses a deep neural network under computer vision, tracks multiple targets in real time through a detection and tracking mode, and simultaneously uses a multi-region method to divide a region of a speed skating field, so that the movement characteristics of speed skating players in the region accord with certain linear or curve characteristics. The method corrects the existing multi-target tracking method according to the motion characteristics of the existing fast skating players, so that the multi-target tracking task of the fast skating players can be completed more quickly and better.
Drawings
FIG. 1 is a flow chart of an implementation of a region-based multi-feature matching skater multi-target tracking method;
FIG. 2 is a schematic diagram of zone segmentation for a zone-based multi-feature matching skater multi-target tracking method;
detailed description of the invention
The invention will be described in further detail with reference to the accompanying figure 1 and the following detailed description:
referring to fig. 1, a multi-target tracking method for a multi-feature matching speed skating player based on zones includes the following steps:
s1, dividing the current area according to the speed skating site, wherein the divided areas are shown in the attached figure 2;
in order to improve the detection accuracy and obtain a better multi-target tracking result, the sports field is divided into 8 areas according to the motion characteristics of different speed gliders in the field, and pure linear or curve tasks are carried out among the areas.
s2, detecting the information of the fast skaters in the picture according to a target detection algorithm;
in order to better apply various characteristics to track and match the skater, various information of the skater in the picture is utilized in the target detection stage.
s21, the position of the skater and bounding box information, primarily indicating the position of the skater in the picture, and the area in which it is located;
s22, characteristic information of the speed skating players, wherein the motion characteristic information is a group of pedestrian re-identification vectors with the length of N and is used for carrying out characteristic measurement.
s23 posture characteristics of the skater, comprising 17 joints representing the position information of the joints of the skater.
s3, performing zone division on the detected speed skating player information according to the zone in s1, wherein the specific division principle is as follows:
s31, marking the position of the regional characteristic point in the field in the picture, and calculating the regional division used by 4 curves for motion according to the corresponding characteristic point.
The area division process corresponding to the s32 and 4 curves is as follows;
the calculation process is as follows:
1. the real field corresponding to the plane image is an ellipse, and is divided into two parts according to the major axis curve, wherein the left part is marked as 0, and the right part is marked as 1;
2. the three curves are equally taken in parallel with the short axis, the upper part of the first curve is marked as 0, and the lower part of the first curve is marked as 1;
3. the continuation above the second curve is marked as 0 and the continuation below is marked as 1;
4. the continuation above the third curve is marked as 0 and the continuation below is marked as 1;
5. the corresponding eight blocks of regions get the corresponding serial numbers of 0000, 0100, 0110, 0111, 1000, 1100, 1110, 1111.
s33, calculating the area position of the speed skating player according to the position information of the speed skating player, and applying different area matching principles.
s4, matching the speed skiers in each area by using the multi-feature information;
and performing comprehensive characteristic matching measurement by using the region position, the pedestrian re-identification characteristic and the attitude joint point characteristic according to the information of the fast-skating players in each region, and performing association matching on the front frame and the rear frame of the fast-skating players.
And s5, matching the areas according to the tracking result of the speed skating players in each area to form a final tracking result.
Acquiring the tracking result of the common part of the fast skaters between the areas, acquiring the matching relation between the IDs in the front area and the back area in a voting mode in a time period, and matching the areas by using the tracking result of the common part to obtain the final matching result, thereby finally forming a complete tracking track.
It should be understood that the above description is only an overall implementation flow of the present invention, and the present invention is not limited to the above implementation flow, and it should be noted that all equivalent and obvious modifications made by those skilled in the art under the teaching of the present specification fall within the spirit scope of the present specification, and should be protected by the present invention.
Claims (3)
1. A multi-target tracking method for a multi-feature matching speed skating player based on a region is characterized by comprising the following steps:
s1, dividing the current area according to the speed skating site;
s2, detecting the information of the fast skaters in the picture according to a target detection algorithm;
s3, performing zone division on the speed skating player information detected in s2 according to the divided zones in s 1;
s4, matching the speed skiers in each area by using the multi-feature information;
and s5, matching the areas according to the tracking result of the speed skating players in each area to form a final tracking result.
2. The multi-target tracking method for the speed skating players based on the zone of claim 1, wherein in the steps s1 and s3, the sports ground is divided into zones, and a multi-feature tracking strategy is applied to different zones, so that the tracking performance of the speed skating players is improved.
3. The method as claimed in claim 1, wherein the step s4 is performed by performing a comprehensive metric matching between frames according to the multi-feature matching technique, the region location information, the pedestrian re-identification feature and the posture feature.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1738426A (en) * | 2005-09-09 | 2006-02-22 | 南京大学 | Video motion goal division and track method |
| US20110115920A1 (en) * | 2009-11-18 | 2011-05-19 | Industrial Technology Research Institute | Multi-state target tracking mehtod and system |
| CN109410245A (en) * | 2018-09-13 | 2019-03-01 | 北京米文动力科技有限公司 | A kind of video target tracking method and equipment |
| CN109448025A (en) * | 2018-11-09 | 2019-03-08 | 国家体育总局体育科学研究所 | Short-track speeding skating sportsman's automatically tracks and track modeling method in video |
| CN110490901A (en) * | 2019-07-15 | 2019-11-22 | 武汉大学 | The pedestrian detection tracking of anti-attitudes vibration |
| CN111639570A (en) * | 2020-05-20 | 2020-09-08 | 华中科技大学 | Online multi-target tracking method based on motion model and single-target clue |
| CN111709974A (en) * | 2020-06-22 | 2020-09-25 | 苏宁云计算有限公司 | Human body tracking method and device based on RGB-D image |
| CN111882580A (en) * | 2020-07-17 | 2020-11-03 | 元神科技(杭州)有限公司 | Video multi-target tracking method and system |
-
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- 2021-10-20 CN CN202111220338.4A patent/CN113963375A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1738426A (en) * | 2005-09-09 | 2006-02-22 | 南京大学 | Video motion goal division and track method |
| US20110115920A1 (en) * | 2009-11-18 | 2011-05-19 | Industrial Technology Research Institute | Multi-state target tracking mehtod and system |
| CN109410245A (en) * | 2018-09-13 | 2019-03-01 | 北京米文动力科技有限公司 | A kind of video target tracking method and equipment |
| CN109448025A (en) * | 2018-11-09 | 2019-03-08 | 国家体育总局体育科学研究所 | Short-track speeding skating sportsman's automatically tracks and track modeling method in video |
| CN110490901A (en) * | 2019-07-15 | 2019-11-22 | 武汉大学 | The pedestrian detection tracking of anti-attitudes vibration |
| CN111639570A (en) * | 2020-05-20 | 2020-09-08 | 华中科技大学 | Online multi-target tracking method based on motion model and single-target clue |
| CN111709974A (en) * | 2020-06-22 | 2020-09-25 | 苏宁云计算有限公司 | Human body tracking method and device based on RGB-D image |
| CN111882580A (en) * | 2020-07-17 | 2020-11-03 | 元神科技(杭州)有限公司 | Video multi-target tracking method and system |
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