Multi-panorama camera target tracking method and device, computer device, and storage medium

Abstract

The present disclosure relates to a multi-panoramic camera target tracking method, device, computer equipment and storage medium. The multi-panoramic camera comprises a main panoramic camera and a plurality of auxiliary panoramic cameras, the main panoramic camera is installed on an amusement vehicle, and the method comprises the following steps: receiving first feature information sent by the main panoramic camera; the first feature information is feature information of a target person riding the amusement vehicle photographed by the main panoramic camera; performing pedestrian re-identification on the photographed person according to the first feature information, determining a matching person in the person photographed by the auxiliary panoramic camera which matches the first feature information of the target person; receiving a first clustering center determined after clustering of the first feature information sent by the main panoramic camera, determining second feature information corresponding to the matching person, clustering the second feature information, and determining a second clustering center; and performing target tracking on the target person photographed by the auxiliary panoramic camera according to the first clustering center and the second clustering center.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of image processing, and in particular to a multi-panoramic camera target tracking method and device, computer equipment and storage medium. BACKGROUND

[0002] With the development of mobile portable shooting devices, for karting, amusement park cars and other entertainment and leisure scenes, the range of positions where tourists appear is large, and the angles of human body orientations change a lot. If a tourist wants to record the exciting moments in the process of playing, using a single mobile portable shooting device may not be able to record the exciting moments completely. In this regard, multiple mobile portable shooting devices can be installed in karting, amusement park cars and other entertainment and leisure scenes to identify tourists and record various exciting moments of tourists.

[0003] However, when multiple mobile portable shooting devices identify tourists, the tourists may cause occlusion in the entertainment scene, so that the body cannot be fully displayed. Therefore, how to accurately identify the target tourist and derive video data matching the target tourist is a problem to be solved. SUMMARY

[0004] Therefore, it is necessary to provide a multi-panoramic camera target tracking method, device, computer equipment and storage medium to solve the above technical problems.

[0005] In a first aspect, the present disclosure provides a multi-panoramic camera target tracking method. The multi-panoramic camera includes a main panoramic camera and multiple secondary panoramic cameras, the main panoramic camera is installed on an amusement vehicle, and the method includes:

[0006] receiving first feature information sent by the main panoramic camera; the first feature information is feature information of a target person riding the amusement vehicle photographed by the main panoramic camera;

[0007] performing pedestrian re-identification on the photographed person according to the first feature information, to determine a matching person in the persons photographed by the secondary panoramic cameras that matches the first feature information of the target person;

[0008] receiving a first cluster center determined after clustering of the first feature information sent by the main panoramic camera, and determining second feature information corresponding to the matching person and clustering the second feature information to determine a second cluster center;

[0009] performing target tracking on the target person photographed by the secondary panoramic cameras according to the first cluster center and the second cluster center.

[0010] In one of the embodiments, the target tracking of the target person in the sub-panoramic camera according to the first clustering center and the second clustering center comprises:

[0011] calculating the similarity between the first clustering center and the second clustering center;

[0012] filtering the second clustering center according to the similarity and a pre-set similarity threshold to determine a target clustering center in the second clustering center;

[0013] determining target feature information matched with the target clustering center, and determining the target person in the sub-panoramic camera according to the target feature information to track the target person.

[0014] In one of the embodiments, the first feature information is obtained in the following manner:

[0015] determining a plurality of panoramic images captured by the main panoramic camera, and extracting person feature information of a person in each panoramic image;

[0016] constructing a person-time relationship matrix according to the person feature information of the person in each panoramic image and the time of each panoramic image;

[0017] calculating the similarity score between the person feature information at different times in the person-time relationship matrix;

[0018] determining the first feature information of a target person in the person-time relationship matrix according to the similarity score and a pre-determined correlation algorithm.

[0019] In one of the embodiments, the target tracking of the target person in the sub-panoramic camera according to the first clustering center and the second clustering center comprises:

[0020] receiving first time information corresponding to the first clustering center in each first panoramic image sent by the main panoramic camera, the first panoramic image being an image captured by the main panoramic camera;

[0021] determining second time information corresponding to the second clustering center in each second panoramic image, the second panoramic image being an image captured by the sub-panoramic camera;

[0022] constructing a first correlation matrix according to the first clustering center and the first time information, and constructing a second correlation matrix according to the second clustering center and the second information;

[0023] calculate a similarity between the first correlation matrix and the second correlation matrix, filter the second correlation matrix according to the similarity, and determine a target element in the second correlation matrix;

[0024] According to the target element, determine a target person photographed by the secondary panoramic camera, and perform target tracking on the target person.

[0025] In one of the embodiments, after the target tracking on the target person, the method further comprises:

[0026] determine a plurality of panoramic images containing the target person photographed by the secondary panoramic camera;

[0027] send the plurality of panoramic images containing the target person to the primary panoramic camera, to instruct the primary panoramic camera to perform video editing according to the plurality of panoramic images and a panoramic image containing the target person photographed by the primary panoramic camera, to obtain a video containing the target person.

[0028] In one of the embodiments, before the receiving of the first feature information sent by the primary panoramic camera, the method further comprises:

[0029] send a connection request to the primary panoramic camera, the connection request being used to instruct the primary panoramic camera to establish a connection with the secondary panoramic camera.

[0030] In a second aspect, the disclosure also provides a multi-panoramic camera target tracking device. The multi-panoramic camera comprises a primary panoramic camera and a plurality of secondary panoramic cameras, the primary panoramic camera being installed on an amusement vehicle, and the device comprises:

[0031] an information receiving module, configured to receive first feature information sent by the primary panoramic camera; the first feature information being feature information of a target person riding the amusement vehicle photographed by the primary panoramic camera;

[0032] a matching person determining module, configured to perform pedestrian re-identification on a person photographed according to the first feature information, and determine a matching person in the person photographed by the secondary panoramic camera that matches the first feature information of the target person;

[0033] a center point determining module, configured to receive a first clustering center determined after clustering of the first feature information sent by the primary panoramic camera, and determine second feature information corresponding to the matching person and cluster the second feature information to determine a second clustering center;

[0034] a target tracking module configured to track the target person in the sub-panoramic camera according to the first clustering center and the second clustering center.

[0035] In a third aspect, the present disclosure provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program. The processor implements the steps in any of the above method embodiments when executing the computer program.

[0036] In a fourth aspect, the present disclosure provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. The computer program, when executed by a processor, implements the steps in any of the above method embodiments.

[0037] In a fifth aspect, the present disclosure provides a computer program product. The computer program product includes a computer program. The computer program, when executed by a processor, implements the steps in any of the above method embodiments.

[0038] In the above embodiments, the first feature information sent by the main panoramic camera is received. The first feature information is feature information of a target person riding the amusement vehicle, which is captured by the main panoramic camera. The person captured is identified by pedestrian re-identification according to the first feature information, and a matching person in the person captured by the sub-panoramic camera, which matches the first feature information of the target person, is determined. Pedestrian re-identification can accurately identify the person by the first feature information, so as to obtain the matching person, thereby ensuring the accuracy of subsequent target tracking. In addition, in the case of possible misidentification of pedestrian re-identification, if misidentification occurs, the error person will be followed in the subsequent tracking process, resulting in failure to track the target person. Therefore, in order to avoid this situation, the second feature information of the matching person can be determined, the second feature information is clustered to obtain the second clustering center, and the matching person can be further screened according to the first clustering center and the second clustering center, so as to determine the target person. The target person captured in the sub-panoramic camera is tracked, and the wonderful moment of the target person can be accurately recorded. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the specific embodiments of the present disclosure or the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present disclosure, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0040] Figure 1Fig. 1 is a schematic diagram of an application environment of a multi-panoramic camera target tracking method in an embodiment;

[0041] Figure 2 Fig. 2 is a schematic diagram of a flow of a multi-panoramic camera target tracking method in an embodiment;

[0042] Figure 3 Fig. 3 is a schematic diagram of a flow of S208 in an embodiment;

[0043] Figure 4 Fig. 4 is a schematic diagram of a flow of obtaining first feature information in an embodiment;

[0044] Figure 5 Fig. 5 is another schematic diagram of a flow of S208 in an embodiment;

[0045] Figure 6 Fig. 6 is a schematic diagram of a flow after S208 in an embodiment

[0046] Figure 7 Fig. 7 is a schematic diagram of a structure of a multi-panoramic camera target tracking device in an embodiment;

[0047] Figure 8 Fig. 8 is a schematic diagram of an internal structure of a computer device in an embodiment. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present disclosure clearer, further detailed description will be made to the present disclosure in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure, and are not used to limit the present disclosure.

[0049] It should be noted that the terms "first", "second", etc. in the specification and claims and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, device, product or equipment including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0050] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" could mean: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the related objects.

[0051] The present disclosure provides a multi-panoramic camera target tracking method, which can be applied to Figure 1 In the application environment shown. The main panoramic camera 102 communicates with multiple sub-panoramic cameras 104. The main panoramic camera 102 is typically installed on an amusement vehicle. The sub-panoramic camera 104 receives first feature information sent by the main panoramic camera 102. The first feature information is feature information of a target person riding in the amusement vehicle captured by the main panoramic camera 102. The sub-panoramic camera 104 performs person re-identification on the captured person based on the first feature information, and determines a matching person among the persons captured by the sub-panoramic camera 104 that matches the first feature information of the target person. The sub-panoramic camera 104 receives a first cluster center obtained by clustering the first feature information sent by the main panoramic camera 102, and determines second feature information corresponding to the matching person and clusters the second feature information to obtain a second cluster center. The sub-panoramic camera 104 tracks the target person captured by the sub-panoramic camera based on the first cluster center and the second cluster center.

[0052] In one embodiment, Figure 2 As shown, a multi-panoramic camera target tracking method is provided, which is applied to Figure 1 Taking the secondary panoramic camera 104 in the figure as an example, the multi-panoramic camera may include: a main panoramic camera and a secondary panoramic camera. The main panoramic camera is usually installed on an amusement vehicle, mainly to shoot the scenes of tourists or users riding in the amusement vehicle and driving the amusement vehicle. The secondary panoramic camera can be installed in various locations in entertainment and leisure venues, such as the boundary of a racing track, other amusement vehicles, and so on. It should be noted that in some embodiments of the present disclosure, the panoramic camera installed on the vehicle that the target person is riding in may be the main panoramic camera, and the panoramic cameras installed on the remaining amusement vehicles may be secondary panoramic cameras. Depending on the amusement vehicle that the target person is riding in, the main panoramic camera and the secondary panoramic camera will change accordingly, and the amusement vehicles may include: karts, sightseeing cars, cable cars, and so on. The method comprises the following steps:

[0053] S202, receiving first feature information sent by the main panoramic camera; the first feature information is feature information of a target person riding in the amusement vehicle captured by the main panoramic camera.

[0054] The feature information of the target person refers to various attributes and features used to describe and identify the target person, and can include limb features, appearance features, clothing features, and the like.

[0055] Specifically, since the main panoramic camera is installed on the amusement vehicle, when a person gets on the amusement vehicle, the panoramic camera can identify the person getting on the amusement vehicle, which can be the target person. When the main panoramic camera identifies the target person, the feature information of the target person can be identified to obtain first feature information. In addition, the main panoramic camera can also detect whether a secondary panoramic camera exists within a certain range, and if the secondary panoramic camera exists, the first feature information can be sent to the secondary panoramic camera. The secondary panoramic camera can receive the first feature information.

[0056] S204, performing person re-identification on the photographed person according to the first feature information to determine a matching person in the person photographed by the secondary panoramic camera that matches the first feature information of the target person.

[0057] The person re-identification (ReID) is an important research task in the field of computer vision, mainly used to identify the same person between different cameras. The goal of ReID is to identify the same individual under different angles, different lighting conditions, and different backgrounds.

[0058] Specifically, the secondary panoramic camera can perform person re-identification on the panoramic image it photographs using the first feature information, thereby determining a matching person in the panoramic image it photographs that matches the first feature.

[0059] S206, receiving the first clustering center determined after clustering of the first feature information sent by the main panoramic camera, and determining second feature information corresponding to the matching person and clustering the second feature information to determine a second clustering center.

[0060] The cluster center is a key concept in cluster analysis, which refers to the center position or representative point of each cluster (or cluster) in the clustering algorithm. The calculation of the cluster center is usually based on the features of all sample points in the cluster, and is used to represent the core features of the cluster.

[0061] Specifically, after obtaining the first feature information, the main panoramic camera can cluster the first feature information to determine a first clustering center. When clustering, K-means, hierarchical clustering, DBSCAN (density-based clustering), and other clustering algorithms can be used. Similarly, after determining the matched person, the secondary panoramic camera can identify second feature information of the matched person. Then, the second feature information is clustered to obtain a second clustering center.

[0062] In S208, target tracking is performed on the target person photographed by the secondary panoramic camera according to the first clustering center and the second clustering center.

[0063] Object Tracking is an important task in the field of computer vision and image processing, aiming to continuously detect and track the movement of a specific object from a video sequence or consecutive frames.

[0064] Specifically, the second clustering center that matches the first clustering center can be determined from the second clustering center according to the first clustering center. Then, the target person is determined using the second clustering center that matches the first clustering center. After determining the target person, the secondary panoramic camera can perform target tracking on the target person within the recognition range.

[0065] In the above multi-panoramic camera target tracking method, the first feature information sent by the main panoramic camera is received. The first feature information is the feature information of the target person riding the amusement vehicle photographed by the main panoramic camera. According to the first feature information, pedestrian re-identification is performed on the photographed person to determine the matched person in the person photographed by the secondary panoramic camera that matches the first feature information of the target person. Pedestrian re-identification can accurately identify the person through the first feature information to obtain the matched person, thereby ensuring the accuracy of subsequent target tracking. In addition, due to the possibility of misidentification in pedestrian re-identification, if misidentification occurs, the error person will be followed during the tracking process, resulting in the inability to track the target person. Therefore, in order to avoid this situation, the second feature information of the matched person can also be determined, the second feature information is clustered to obtain a second clustering center, and according to the first clustering center and the second clustering center, the matched person can be further screened to determine the target person. Target tracking is performed on the target person photographed by the secondary panoramic camera, which can accurately record the exciting moments of the target person.

[0066] In one embodiment, as Figure 3As shown, the target tracking on the target person in the sub-panoramic camera according to the first cluster center and the second cluster center comprises:

[0067] S302, similarity between the first cluster center and the second cluster center is calculated.

[0068] Specifically, Euclidean distance between the first cluster center and the second cluster center can be calculated, and the similarity can be determined according to the Euclidean distance. Cosine similarity between the first cluster center and the second cluster center can also be calculated, and the similarity can be determined according to the cosine similarity. In addition, if the first cluster center and the second cluster center are binary features, the similarity between the first cluster center and the second cluster center can be calculated using the Jaccard similarity coefficient.

[0069] S304, the second cluster center is filtered according to the similarity and a pre-set similarity threshold, and a target cluster center in the second cluster center is determined.

[0070] Specifically, the similarity can be filtered using the similarity threshold, and the similarity greater than the similarity threshold can be determined. Then, the second cluster center corresponding to the similarity greater than the similarity threshold is determined. The second center point is the target cluster center after filtering.

[0071] S306, target feature information matched with the target cluster center is determined, and the target person in the sub-panoramic camera is determined according to the target feature information, and the target tracking on the target person is performed.

[0072] Specifically, after the target cluster center is determined, the second feature information matched with the target cluster center can be found, and the second feature information can be determined as the target feature information. The person generating the target feature information can be found, and the person can be the target person. After the target person is determined, the target person can be tracked.

[0073] In this embodiment, by calculating the similarity, the target cluster center is determined using the similarity threshold, and the target person is determined, so that the target person can be more accurately determined, and the accuracy of the target tracking is improved.

[0074] In one embodiment, as shown, Figure 4 The first feature information comprises the following:

[0075] S402, a plurality of panoramic images captured by the main panoramic camera are determined, and person feature information of a person contained in each panoramic image is extracted.

[0076] Specifically, during the shooting process, for each frame of the panoramic image obtained by shooting, the person in each frame of the panoramic image can be identified first, and then the person feature information of the person is determined. The person in the panoramic image can be identified by image segmentation, or a neural network model such as YOLO (You Only Look Once) can be used for identification.

[0077] S404, constructing a person-time relationship matrix according to the person feature information of the person contained in each frame of the panoramic image and the time of each frame of the panoramic image.

[0078] Specifically, the association matrix can be constructed according to the person feature information of each frame of the image and the time information of each frame of the panoramic image. The association matrix can include: the person feature information contained under each time information.

[0079] S406, calculating the similarity score between the person feature information at different times in the person-time relationship matrix.

[0080] Specifically, for the target at different times, the similarity score between the person feature information at different times can be calculated.

[0081] S408, determining the first feature information of the target person contained in the person-time relationship matrix according to the similarity score and a predetermined association algorithm.

[0082] The association algorithm can include: Hungarian algorithm, Kalman filter, multiple hypothesis tracking (MHT) algorithm, etc.

[0083] Specifically, the relevant person feature information can be determined according to the similarity score and the association algorithm, and the same person feature information between different frames of panoramic images is determined according to the relevant person feature information. The first feature information is determined according to the person feature information appearing in each frame of the panoramic image.

[0084] In some exemplary embodiments, taking two frames of panoramic images as an example, taking two frames of panoramic images t and t+1 as an example, an association matrix A can be created, and the size of the matrix is MxN, where M is the first feature information in t, and N is the first feature information in time t+1. The element A[i][j] of the matrix represents the similarity score between the first feature information of the i-th target in t and the first feature information of the j-th target in t+1. Then the association matrix can be input into the association algorithm, and the first feature information is determined by using the association algorithm.

[0085] For example, in actual application, it is assumed that we detect 3 targets at t, and 4 targets at t+1: the person feature information of t is:

[0086] Target 1: [Feature 1.1, Feature 1.2]

[0087] Target 2: [Feature 2.1, Feature 2.2]

[0088] Target 3: [Feature 3.1, Feature 3.2]

[0089] The object feature information at t+1 is:

[0090] Target A: [Feature A.1, Feature A.2]

[0091] Target B: [Feature B.1, Feature B.2]

[0092] Target C: [Feature C.1, Feature C.2]

[0093] Target D: [Feature D.1, Feature D.2]

[0094] By calculating the similarity between these targets, a 3x4 association matrix can be obtained, which contains the similarity score between each target, facilitating subsequent matching and tracking.

[0095] In this embodiment, by constructing a person time relationship matrix and calculating a similarity score, each frame of panoramic image captured by the main panoramic camera can be accurately analyzed, so that the first feature information can be accurately recognized, and the accuracy of subsequent target tracking is ensured.

[0096] In one embodiment, as shown in Figure 5 According to the first cluster center and the second cluster center, the target tracking of the target person captured in the auxiliary panoramic camera includes:

[0097] S502, receiving the first time information corresponding to the first cluster center in each frame of the first panoramic image sent by the main panoramic camera, the first panoramic image being an image captured by the main panoramic camera.

[0098] Specifically, since the main panoramic camera captures panoramic video when shooting, the panoramic video will include multiple frames of panoramic images. Therefore, each frame of the first panoramic image usually has its corresponding first cluster center. The first time information corresponding to the first cluster center in each frame of the first panoramic image can be determined.

[0099] S504, determining the second time information corresponding to the second cluster center in each frame of the second panoramic image, the second panoramic image being an image captured by the auxiliary panoramic camera.

[0100] Specifically, the same sub-panoramic camera also takes panoramic videos when taking panoramic videos. Therefore, each frame of the second panoramic image also has a corresponding second cluster center. The second time information corresponding to each frame of the second panoramic image can be determined.

[0101] S506, constructing a first association matrix according to the first cluster center and the first time information, and constructing a second association matrix according to the second cluster center and the second time information.

[0102] S508, calculating the similarity between the first association matrix and the second association matrix, screening the second association matrix according to the similarity, and determining the target element in the second association matrix.

[0103] Specifically, the first association matrix can be constructed according to the first cluster center and the first time information. The second association matrix can be constructed according to the second cluster center and the second time. The similarity of each element in the first association matrix and the second association matrix can be calculated, and then the elements in the second association matrix are screened by using the similarity to obtain the target element.

[0104] In some exemplary embodiments, for example, the information in the first association matrix can generally include:

[0105] S1 element: [A1 first cluster center, B1 first time information]

[0106] S2 element: [A2 first cluster center, B2 first time information]

[0107] For example, the information in the second association matrix can include:

[0108] W1 element: [C1 second cluster center, D1 second time information]

[0109] W2 element: [C2 second cluster center, D2 second time information]

[0110] W3 element: [C3 second cluster center, D3 second time information]

[0111] W4 element: [C4 second cluster center, D4 second time information]

[0112] The similarity of the W1 element to the S1 element, the similarity of the W2 element to the S1 element, the similarity of the W3 element to the S1 element, the similarity of the W4 element to the S1 element, the similarity of the W2 element to the S1 element, the similarity of the W2 element to the S2 element, the similarity of the W3 element to the S2 element, and the similarity of the W2 element to the S2 element can be calculated respectively, for example, the similarity of the W1 element to the S1 element is 0.8, the similarity of the W2 element to the S1 element is 0.45, the similarity of the W3 element to the S1 element is 0.2, the similarity of the W4 element to the S1 element is 0.3, the similarity of the W2 element to the S1 element is 0.75, the similarity of the W2 element to the S2 element is 0.8, the similarity of the W3 element to the S2 element is 0.95, and the similarity of the W2 element to the S2 element is 0.1. The first correlation matrix can be taken as a standard, and since there are only two elements in the first correlation matrix, two elements also need to be selected from the second correlation matrix, and the two elements with the highest similarity to S1 and S2 can be selected as target elements according to the similarity, the element with the highest similarity to S1 is the W1 element, and the element with the highest similarity to S2 is the W3 element, and the W1 element and the W3 element can be the target elements.

[0113] S510, determining a target person photographed by the secondary panoramic camera according to the target element, and performing target tracking on the target person.

[0114] Specifically, the second time information and the second clustering center can be determined according to the target element, and then the target person photographed by the secondary panoramic camera is determined according to the determined second time information and the second clustering center, and target tracking is performed on the target person.

[0115] In this embodiment, the correlation matrix is generated by using time information, so that the similarity of the first correlation matrix and the second correlation matrix is calculated, which can ensure that the time information is used as a judgment condition in the process of finally determining the target person, and the accuracy of the identification of the target person is ensured.

[0116] In one embodiment, as shown in Figure 6 After the target tracking on the target person is performed, the method further includes:

[0117] S602, determining a plurality of frames of panoramic images containing the target person photographed by the secondary panoramic camera.

[0118] S604, sending the plurality of frames of panoramic images containing the target person to the primary panoramic camera, to instruct the primary panoramic camera to perform video editing according to the plurality of frames of panoramic images and panoramic images containing the target person photographed by the primary panoramic camera, to obtain a video containing the target person.

[0119] Specifically, after the sub-panoramic camera performs target tracking on the target person, the sub-panoramic camera can further send the multiple frames of panoramic images (which can be panoramic videos) containing the target person to the main panoramic camera. The user can perform video editing on the multiple frames of panoramic images containing the target person captured by the main panoramic camera and the received panoramic images containing the target person in the main panoramic camera, so as to obtain a video containing the target person. The video usually records the wonderful moments of the person.

[0120] In this embodiment, the sub-panoramic camera can send the panoramic images containing the target person to the main panoramic camera, and all the panoramic images of the target person can be integrated, which is convenient for subsequent editing.

[0121] In one embodiment, before the receiving the first feature information sent by the main panoramic camera, the method further comprises:

[0122] sending a connection request to the main panoramic camera, the connection request being used to instruct the main panoramic camera to establish a connection with the sub-panoramic camera.

[0123] Specifically, the main panoramic camera and the sub-panoramic camera can detect other panoramic cameras (including the main panoramic camera and the sub-panoramic camera) within their signal ranges, and can send a connection request to the other panoramic cameras when detecting the other panoramic cameras. The connection can be a wireless connection, such as a Bluetooth connection, a WIFI connection, etc.

[0124] It should be understood that, although each step in the flowchart involved in each of the above embodiments is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each of the above embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0125] Based on the same inventive concept, the disclosure embodiments also provide a multi-panoramic camera target tracking device for implementing the above-mentioned multi-panoramic camera target tracking method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, and therefore the specific limitations in one or more multi-panoramic camera target tracking device embodiments provided below can refer to the limitations of the multi-panoramic camera target tracking method described above, which will not be repeated here.

[0126] In one embodiment, asFigure 7 As shown, a multi-panoramic camera target tracking apparatus 700 is provided, the multi-panoramic camera comprising a main panoramic camera and a plurality of auxiliary panoramic cameras, the main panoramic camera being mounted on an amusement vehicle, the apparatus comprising an information receiving module 702, a matching person determining module 704, a center point determining module 706 and a target tracking module 708, wherein:

[0127] The information receiving module 702 is configured to receive first feature information sent by the main panoramic camera, the first feature information being feature information of a target person riding the amusement vehicle photographed by the main panoramic camera.

[0128] The matching person determining module 704 is configured to perform pedestrian re-identification on the photographed person according to the first feature information, and determine a matching person in the persons photographed by the auxiliary panoramic cameras that matches the first feature information of the target person.

[0129] The center point determining module 706 is configured to receive a first clustering center determined after clustering of the first feature information sent by the main panoramic camera, and determine second feature information corresponding to the matching person and cluster the second feature information to determine a second clustering center.

[0130] The target tracking module 708 is configured to perform target tracking on the target person photographed by the auxiliary panoramic cameras according to the first clustering center and the second clustering center.

[0131] In an embodiment of the apparatus, the target tracking module 708 comprises:

[0132] A first similarity calculating module is configured to calculate a similarity between the first clustering center and the second clustering center.

[0133] A screening module is configured to screen the second clustering center according to the similarity and a pre-set similarity threshold to determine a target clustering center in the second clustering center.

[0134] A tracking sub-module is configured to determine target feature information matching the target clustering center, determine a target person photographed by the auxiliary panoramic cameras according to the target feature information, and perform target tracking on the target person.

[0135] In an embodiment of the apparatus, the apparatus further comprises:

[0136] The first feature information determination module is configured to determine a plurality of panoramic images captured by the main panoramic camera, extract the person feature information of a person contained in each panoramic image, construct a person-time relationship matrix according to the person feature information of the person contained in each panoramic image and the time of each panoramic image, calculate the similarity score between the person feature information at different times in the person-time relationship matrix, and determine the first feature information of a target person contained in the person-time relationship matrix according to the similarity score and a pre-determined association algorithm.

[0137] In an embodiment of the apparatus, the target tracking module 708 includes:

[0138] The time information receiving module is configured to receive first time information corresponding to the first cluster center in each first panoramic image sent by the main panoramic camera, the first panoramic image being an image captured by the main panoramic camera.

[0139] The time information determination module is configured to determine second time information corresponding to the second cluster center in each second panoramic image, the second panoramic image being an image captured by the auxiliary panoramic camera.

[0140] The association matrix construction module is configured to construct a first association matrix according to the first cluster center and the first time information, and construct a second association matrix according to the second cluster center and the second information.

[0141] The second similarity calculation module is configured to calculate the similarity between the first association matrix and the second association matrix, filter the second association matrix according to the similarity, and determine a target element in the second association matrix.

[0142] The person tracking module is configured to determine a target person captured by the auxiliary panoramic camera according to the target element, and perform target tracking on the target person.

[0143] In an embodiment of the apparatus, the apparatus further includes an image sending module configured to determine a plurality of panoramic images containing the target person captured by the auxiliary panoramic camera, and send the plurality of panoramic images containing the target person to the main panoramic camera, so as to instruct the main panoramic camera to perform video editing according to the plurality of panoramic images and a panoramic image containing the target person captured by the main panoramic camera, to obtain a video containing the target person.

[0144] In an embodiment of the apparatus, the apparatus further includes:

[0145] The request sending module is used to send a connection request to the main panoramic camera, where the connection request is used to instruct the main panoramic camera to establish a connection with the secondary panoramic camera.

[0146] Each module in the multi-panoramic camera target tracking device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0147] In one embodiment, a computer device is provided. The computer device may be a terminal (eg, a panoramic camera), and its internal structure may be as shown in FIG. Figure 8 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a multi-panoramic camera target tracking method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0148] Those skilled in the art will understand that Figure 8 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present disclosure, and does not constitute a limitation on the computer device to which the solution of the present disclosure is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0149] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0150] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0151] In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0152] It should be noted that the panoramic images (including the panoramic images captured by the primary panoramic camera and the panoramic images captured by the secondary panoramic camera) involved in the present disclosure are all information and data authorized by the user or fully authorized by all parties.

[0153] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present disclosure can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present disclosure can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0154] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present disclosure.

[0155] The above-described embodiments are merely illustrative of several embodiments of the present disclosure, which are described in a relatively specific and detailed manner, but should not be construed as limiting the scope of the patent of the present disclosure. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present disclosure, which are all within the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be subject to the appended claims.

Claims

1. A multi-panorama camera target tracking method, characterized by, The multi-panoramic camera comprises a main panoramic camera and a plurality of auxiliary panoramic cameras, the main panoramic camera is installed on the amusement vehicle, and the method comprises: receiving first feature information sent by the main panoramic camera; the first feature information is feature information of a target person riding the amusement vehicle photographed by the main panoramic camera; performing pedestrian re-identification on the photographed person according to the first feature information to determine a matching person in the person photographed by the auxiliary panoramic camera that matches the first feature information of the target person; receiving a first clustering center determined after the first feature information sent by the main panoramic camera is clustered, determining second feature information corresponding to the matching person, and clustering the second feature information to determine a second clustering center, wherein the second feature information is determined by the auxiliary panoramic camera; performing target tracking on the target person photographed by the auxiliary panoramic camera according to the first clustering center and the second clustering center; the target tracking on the target person photographed by the auxiliary panoramic camera according to the first clustering center and the second clustering center comprises: calculating the similarity of the first clustering center and the second clustering center; screening the second clustering center according to the similarity and a pre-set similarity threshold to determine a target clustering center in the second clustering center; determining target feature information matching the target clustering center, determining a target person photographed by the auxiliary panoramic camera according to the target feature information, and performing target tracking on the target person.

2. The method of claim 1, wherein, The first feature information is obtained in the following manner: determining a plurality of panoramic images photographed by the main panoramic camera, extracting person feature information of a person contained in each panoramic image; constructing a person-time relationship matrix according to the person feature information of the person contained in each panoramic image and the time of each panoramic image; calculating the similarity score between the person feature information at different times in the person-time relationship matrix; determining the first feature information of a target person contained in the person-time relationship matrix according to the similarity score and a pre-determined correlation algorithm.

3. The method of claim 1, wherein, After the target tracking on the target person, the method further comprises: determining a plurality of panoramic images containing the target person photographed by the auxiliary panoramic camera; sending the plurality of panoramic images containing the target person to the main panoramic camera to instruct the main panoramic camera to perform video editing according to the plurality of panoramic images and panoramic images containing the target person photographed by the main panoramic camera to obtain a video containing the target person.

4. The method of claim 3, wherein, Before receiving the first feature information sent by the main panoramic camera, the method further comprises: sending a connection request to the main panoramic camera, wherein the connection request is used to instruct the main panoramic camera to establish a connection with the auxiliary panoramic camera.

5. A multi-panorama camera target tracking method, characterized by, The multi-panoramic camera comprises a main panoramic camera and a plurality of auxiliary panoramic cameras, the main panoramic camera is installed on the amusement vehicle, and the method comprises: receiving first feature information sent by the main panoramic camera; the first feature information is feature information of a target person riding in the amusement vehicle captured by the main panoramic camera; performing person re-identification on the captured person according to the first feature information, and determining a matching person among the persons captured by the secondary panoramic camera that matches the first feature information of the target person; receiving a first cluster center determined after clustering the first feature information sent by the primary panoramic camera, and determining second feature information corresponding to the matching person and clustering the second feature information to determine a second cluster center, wherein the second feature information is determined using the secondary panoramic camera; tracking the target person captured by the secondary panoramic camera according to the first cluster center and the second cluster center; receiving first time information corresponding to the first cluster center in each frame of the first panoramic image sent by the main panoramic camera, where the first panoramic image is an image captured by the main panoramic camera; determining second time information corresponding to the second cluster center in each frame of a second panoramic image, where the second panoramic image is an image captured by the secondary panoramic camera; constructing a first association matrix based on the first cluster center and the first time information, and constructing a second association matrix based on the second cluster center and the second time information; Calculating the similarity between the first incidence matrix and the second incidence matrix, screening the second incidence matrix according to the similarity, and determining a target element in the second incidence matrix; According to the target element, a target person photographed by the secondary panoramic camera is determined, and target tracking is performed on the target person.

6. A multi-panorama camera target tracking apparatus, characterized by, The multi-panoramic camera includes: a main panoramic camera and a plurality of sub-panoramic cameras, wherein the main panoramic camera is installed on the amusement vehicle, and the device includes: an information receiving module, configured to receive first feature information sent by the main panoramic camera; the first feature information is feature information of a target person riding in the amusement vehicle captured by the main panoramic camera; a matching person determination module, configured to perform person re-identification on the photographed persons based on the first feature information, and determine a matching person among the persons photographed by the secondary panoramic camera that matches the first feature information of the target person; a center point determination module, configured to receive a first cluster center determined after clustering the first feature information sent by the primary panoramic camera, and determine second feature information corresponding to the matching person and cluster the second feature information to determine a second cluster center, wherein the second feature information is determined using the secondary panoramic camera; a target tracking module, configured to track the target person captured by the secondary panoramic camera based on the first cluster center and the second cluster center; The target tracking module includes: A first similarity calculation module, configured to calculate the similarity between the first cluster center and the second cluster center; The screening module is configured to screen the second clustering centers according to the similarity and a pre-set similarity threshold, and determine a target clustering center in the second clustering centers. The tracking sub-module is configured to determine target feature information matched with the target clustering center, determine a target person photographed by the secondary panoramic camera according to the target feature information, and perform target tracking on the target person. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. The processor, when executing the computer program, implements the steps of the method of any one of claims 1 to 4 or claim 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 4 or claim 5.

9. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 4 or claim 5.

Images