CN111539375B - Large-scale crowd behavior aided planning method - Google Patents
Large-scale crowd behavior aided planning method Download PDFInfo
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- CN111539375B CN111539375B CN202010387654.XA CN202010387654A CN111539375B CN 111539375 B CN111539375 B CN 111539375B CN 202010387654 A CN202010387654 A CN 202010387654A CN 111539375 B CN111539375 B CN 111539375B
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Abstract
The invention relates to a large-scale crowd behavior aided planning method, which comprises the following steps: s1 designing crowd behavior effect videos; s2, carrying out dynamic frame sampling on the video showing the crowd behavior effect; s3, generating corresponding point locations according to each sampling frame obtained by sampling, and enabling the number of all the point locations of each image to be equal to the number NUM of individual crowds preset by a user; s4, establishing a point location coordinate mapping relation between every two adjacent sampling frames; s5, obtaining the coordinates and transformation of each crowd individual in the action process according to the time and point location coordinates corresponding to the sampling frames and the point location coordinate mapping relation among the sampling frames, thereby obtaining the crowd action scheme. The invention provides a full-process automatic solution from creative videos to large-scale crowd behavior planning, and the full-process automatic solution is reasonable in design scheme and high in hardware utilization efficiency.
Description
Technical Field
The invention relates to a crowd behavior planning method, in particular to a large-scale crowd behavior auxiliary planning method.
Background
At this stage, computer technology and simulation systems have been widely used to plan the position and behavior of large-scale people, such as large-scale people performing professionally programmed director designs.
The existing system supporting the layout design of large-scale crowd performances mainly comprises a virtual layout prototype system of large-scale square artistic performances of Beijing finishing university (Dingxin Yiyi, system simulation academic, 9 months 2008), a virtual layout and rehearsal prototype system of team exercises of Zhejiang university (Teqing leather, academic thesis), and a virtual formation system of large-scale square artistic performances of Harbin industry university (Yao, academic thesis). The planning and design of the performance population by the systems are realized by images. The director originality is embodied in the form of images, the simulation arrangement system generates point locations according to different images, and plans the path of the individual crowd for transformation among different point locations, thereby realizing the planning and design of the crowd behavior.
With the development of computer technology and animation technology, director creatives have added more and more dynamic elements that can, for example, represent the process of a tree from germination to growth. For such complex dynamic creatives, it is more suitable to express the creative effect in the form of video. A video creative idea is converted into a final large-scale crowd behavior, and a complete and open technical scheme is not found in the existing system.
Disclosure of Invention
The invention aims to provide a large-scale crowd behavior aided planning method aiming at the defects of the prior art, which comprises the following steps:
s1 designing crowd behavior effect videos;
s2 dynamic frame sampling of video showing crowd behavior effects, with frame sampling interval SI ═ min (I)hashAnd c); wherein C is a constant set by a user and represents the maximum sampling frame interval when the video content changes smoothly; i ishashFor a dynamic frame interval, it means that the hash distance between two sampled frames is not greater than a threshold value ThashMaximum frame interval of, ThashPreset by a user;
s3, generating corresponding point locations according to each sampling frame obtained by sampling, and enabling the number of all the point locations of each image to be equal to the number NUM of individual crowds preset by a user;
s4, establishing a point location coordinate mapping relation between every two adjacent sampling frames;
s5, obtaining the coordinates and transformation of each crowd individual in the action process according to the time and point location coordinates corresponding to the sampling frames and the point location coordinate mapping relation among the sampling frames, thereby obtaining the crowd action scheme.
According to a specific implementation manner of the embodiment of the present invention, the method for establishing the point coordinate mapping relationship between every two adjacent sampling frames in step S4 includes:
and establishing a complete bipartite graph by taking the Euclidean distance of the vertexes between every two adjacent sampling frame point bitmaps as the side weight, and realizing the optimal matching between the vertexes in the complete bipartite graph through a minimum weight matching algorithm.
According to a specific implementation manner of the embodiment of the present invention, the method for establishing the point coordinate mapping relationship between every two adjacent sampling frames in step S4 includes:
s41, selecting seeds for neighborhood growth by using the same method for point bitmaps of two adjacent sampling frames, and controlling the number of individuals in a growth area through the same threshold value;
s42, converting the bitmap from a block set to a vertex set by taking the block center as the vertex position in the partitioned block neighborhood in the bitmap;
s43, establishing a complete bipartite graph by taking the Euclidean distance of the block vertexes between the converted point bitmaps as the edge weight, and realizing the optimal matching among the block sets in the complete bipartite graph through a minimum weight matching algorithm;
s44, reducing the threshold standard of region growing, repeating the steps S41-S44 for all matched sub-blocks to carry out next-level block division until each block only contains one individual;
and S45, obtaining the point location coordinate mapping relation between two adjacent sampling frames according to the matching result of the last layer.
According to a specific implementation manner of the embodiment of the invention, when the seeds are selected for neighborhood growth, the seeds are selected from the edge to the middle of the crowd connected domain for neighborhood growth, and if the neighborhood growth process is not connected, the neighborhood search range is expanded.
According to a specific implementation manner of the embodiment of the present invention, the hash distance is a mean hash distance.
According to a specific implementation manner of the embodiment of the invention, the number of the levels of block division is 2-3 layers.
According to a specific implementation of the embodiment of the present invention, the threshold of each middle level is 5% of the number of people in the upper level, and the threshold of the last level is 1.
In another aspect, the present invention further provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a mass-crowd behavior-aided planning method as described above.
In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a large-scale crowd behavior aided planning method as described above.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform a method of assisted large-scale crowd behavior planning as described above.
Advantageous effects
The large-scale crowd behavior aided planning method provided by the invention provides a full-process automation solution from creative videos to large-scale crowd behavior planning, can flexibly and dynamically sample video frames, realizes crowd position transformation through hierarchical mapping, and has the advantages of reasonable design scheme and high hardware utilization efficiency.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a hierarchical matching method according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a hierarchical matching method according to an embodiment of the present invention;
FIG. 4 is a comparison chart of mapping effects of different layer numbers;
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a complete solution for designing a crowd planning effect into a video and implementing a crowd planning process according to the video is provided in an embodiment of the present invention, which includes the following steps:
s1 designing crowd behavior effect videos;
s2 dynamic frame sampling of video showing crowd behavior effects, with frame sampling interval SI ═ min (I)hashAnd c); wherein C is a constant set by a user and represents the maximum sampling frame interval when the video content changes smoothly; i ishashFor a dynamic frame interval, it means that the hash distance between two sampled frames is not greater than a threshold value ThashMaximum frame interval of, ThashPreset by a user;
s3, generating corresponding point locations according to each sampling frame obtained by sampling, and enabling the number of all the point locations of each image to be equal to the number NUM of individual crowds preset by a user;
s4, establishing a point location coordinate mapping relation between every two adjacent sampling frames;
s5, obtaining the coordinates and transformation of each crowd individual in the action process according to the time and point location coordinates corresponding to the sampling frames and the point location coordinate mapping relation among the sampling frames, thereby obtaining the crowd action scheme.
The image is extracted from the video frame, the most common method being a fixed frame interval video frame sampling. For example, if the frame rate of the video is 20 frames/second, and the fixed frame interval can be set to 0.05 second, each frame of the video is taken as a sampling frame. However, this equal-interval sampling method, i.e., the equal-interval time slicing method, is not suitable for large-scale crowd behavior planning. For large-scale people, the loss of performance information is serious in the long-interval uniform time slicing method, so that the later manual modification rate is in a very high position; the short-interval uniform slicing method can obtain a good generation effect, but can cause excessive slicing contents, cause system resource waste and influence the calculation time and the real-time performance which are very important for large-scale crowd calculation.
To solve this problem, the method implemented by the first embodiment adopts a dynamic parameter time slicing method. Frame sampling interval SI min (I)hashAnd c); wherein c is a constant set by a user and represents the maximum sampling frame interval when the video content changes smoothly; i ishashFor dynamic frame spacing, it means that the hash distance between two sampled frames is not greater than a thresholdThashMaximum frame interval of, ThashPreset by the user. T ishashAnd c, the two constants respectively realize reasonable sampling of the video frame from the limiting high-pass level and the limiting low-pass level. When the video content changes drastically, ThashThe loss of performance details caused by overlarge sampling interval can be effectively prevented. When the video content changes slowly, c can prevent the gradual change details in the video from being lost by limiting the maximum frame interval of sampling.
According to a specific implementation manner of the second embodiment of the present invention, the method for establishing the point coordinate mapping relationship between every two adjacent sampling frames in step S4 includes:
and establishing a complete bipartite graph by taking the Euclidean distance of the vertexes between every two adjacent sampling frame point bitmaps as the side weight, and realizing the optimal matching between the vertexes in the complete bipartite graph through a minimum weight matching algorithm.
The point-to-point transformation between two adjacent sampling frames is to generate a full shot set, which contains the corresponding relationship of all performance individuals from sampling frame 1 to sampling frame 2.
The point location corresponding relation between the two point bitmaps is established, and an easily-conceivable method is to find the point with the closest distance of each point location of the point bitmap 1 in the point bitmap 2, but the method can generate a large amount of conflicts and needs manual interaction of a user through a UI (user interface) for processing. The processing may be performed in units of rows or columns, but in the case where the number of rows or columns of dots in the two dot bitmaps is different, the processing needs to be performed manually and individually.
The number of point locations generated by the two images is the same, and in the second embodiment, a complete bipartite graph is established by taking the Euclidean distance of the vertex between the two point location graphs as the edge weight, and the optimal matching between the point locations is realized in the complete bipartite graph through a minimum weight matching algorithm. For example, Kuhn-Munkres algorithm (see paper "Kuhn H W. the Hungarian method for the alignment scheme [ J ]. Naval Research logics, 1955,2(1-2): 83-97." and paper "James Munkres. Algorithms for the alignment and transfer schemes [ J ]. Journal of the Society for Industrial & Applied Matmatics, 1957,5(1): 32-38.") as a least-weight point matching algorithm are well suited for application in the application scenario of crowd mapping.
The method of the second embodiment realizes the automatic generation of the crowd individual position transformation scheme by converting the point position mapping relation into the minimum weight matching problem of the complete bipartite graph. The advantage of this method is to minimize the sum of all edge weights of the complete bipartite graph, i.e. the sum of all individual movement distances. And the method can automatically process by using a computer system and adopting a graphical method, does not need manual intervention and adjustment, and is suitable for designing a large-scale crowd behavior planning scheme.
However, in specific applications, the method of example two also has drawbacks. Since only the overall moving distance is considered, the rationality of the moving route for each individual cannot be guaranteed. In practical application, too long moving distance or unreasonable route for some performance individuals can occur, which not only brings difficulty for training implementation of the performance individuals, but also causes route confusion of the overall crowd pattern in the transformation process, and influences performance effect.
According to a specific implementation manner of the third embodiment of the present invention, as shown in fig. 2, the method for establishing the point coordinate mapping relationship between every two adjacent sampling frames in step S4 includes:
s41, selecting seeds for neighborhood growth by using the same method for point bitmaps of two adjacent sampling frames, and controlling the number of individuals in a growth area through the same threshold value;
s42, converting the bitmap from a block set to a vertex set by taking the block center as the vertex position in the partitioned block neighborhood in the bitmap;
s43, establishing a complete bipartite graph by taking the Euclidean distance of the block vertexes between the converted point bitmaps as the edge weight, and realizing the optimal matching among the block sets in the complete bipartite graph through a minimum weight matching algorithm;
s44, reducing the threshold standard of region growing, repeating the steps S41-S44 for all matched sub-blocks to carry out next-level block division until each block only contains one individual;
and S45, obtaining the point location coordinate mapping relation between two adjacent sampling frames according to the matching result of the last layer.
In the third embodiment, hierarchical mapping is performed on the basis of the second embodiment, and fig. 3 is a schematic diagram of the hierarchical matching method described in the third embodiment. As shown in FIG. 3, piAnd pi+1The two point bitmaps are subjected to block division according to the relation between the geometric position and the neighborhood in the first layer, and each point bitmap is divided into three blocks. When dividing the blocks, two point bitmaps are selected by the same method to perform neighborhood growth, and the growth area is controlled by the same threshold value to contain the number of individuals, so that the two point bitmaps obtain the same number of blocks through block division, and each block has the same number of individuals (for a connected point bitmap, only the number of the blocks obtained by the last neighborhood growth is probably less than the threshold value theoretically. And establishing a complete bipartite graph by taking the central points of the three blocks which are well divided by the first level of the two point maps as vertexes, and realizing optimal matching among the block sets in the complete bipartite graph through a minimum weight matching algorithm. In the first-level block optimal matching of FIG. 3, pi+1Block S of1And piBlock S of2' matching, pi+1Block S of2And piBlock S of1' matching, pi+1Block S of3And piBlock S of3' matching.
Since the divided blocks also include a plurality of individuals, the hierarchical block division can be continued until each block includes only one individual. In FIG. 3, for pi+1Block S of3And piBlock S of3' repeat step S3 to step S6, that is, perform the partition of the second level in two partitions and achieve the optimal matching of the partitions by the least weight matching algorithm in the complete bipartite graph. Matching process through second hierarchy，pi+1Second-level block S of3 S1And piSecond-level block S of3’S2' matching, pi+1Second-level block S of3 S2And piSecond-level block S of3’S3' match … ….
This hierarchical partitioning and matching process is repeated until each block contains only one individual block. When each block only comprises one individual, the position corresponding relation of each individual in the crowd between two point bitmaps is obtained.
The method of the third embodiment performs block division and optimal matching through hierarchical levels, so that the system firstly divides blocks from a macroscopic perspective and establishes optimal matching between the blocks, and after relatively reasonable block mapping is established, the mapping of each actor individual in the blocks is refined. Each actor individual is positioned in a reasonable block, so that the problem of unreasonable routes of some actor individuals is avoided to the maximum extent.
The second benefit of the hierarchical block division and optimal matching is that for images with certain similarity, relatively reasonable block mapping enables the outline of the image to be kept clear basically in the point location transformation process, and the situation of image disorder is avoided. Because the images are obtained by sampling from the video, the threshold value of the sampling ensures that the images can keep certain similarity no matter the images are in the condition of rapid change or slow change, namely the condition that two images suddenly change suddenly and suddenly does not occur. Therefore, by adopting hierarchical block division and optimal matching, the clear image contour can be kept in the whole process of individual movement of people, and the situation of image disorder is avoided.
The hierarchical block division and optimal matching greatly reduces the number of nodes performing optimal matching each time, and also brings higher execution efficiency.
Fig. 4 shows the dynamic effect of the middle process of the performance under different levels of mapping strategies. In the figure, simulation dynamic results are obtained by dividing the simulation dynamic results into two levels and three levels without layering from top to bottom. It can be seen from the figure that the results obtained by the two-layer and three-layer layered structures are basically the same, and the phenomenon of crowd contour destruction does not occur. Due to the fact that enough constraints are not added in the non-layered structure, the whole outline of the crowd is damaged by the square block part individuals in order to pursue the principle of minimum overall consumption of the Kuhn-Munkres algorithm, the original purpose of creative design is violated, and the performance simulation effect is influenced.
According to a specific implementation manner of the embodiment of the invention, when the seeds are selected for neighborhood growth, the seeds are selected from the edge to the middle of the crowd connected domain for neighborhood growth, and if the neighborhood growth process is not connected, the neighborhood search range is expanded. For the connected point bitmaps, seeds are selected by the same method to perform neighborhood growth, and the number of individuals in a growing area is controlled by the same threshold value, so that the two point bitmaps can obtain the same number of blocks through block division, and the corresponding blocks contain the same number of individuals. However, when there is a disconnected situation in the dot bitmaps, there is a possibility that the number of individuals in the block is smaller than the threshold, so that the number of individuals in the corresponding blocks in the two dot bitmaps is inconsistent. As an optimized implementation mode, when the seeds are selected for neighborhood growth, the seeds are selected from the edge to the middle of the crowd connected domain for neighborhood growth, and if the situation of non-connection occurs in the neighborhood growth process, the neighborhood search range is expanded. By the method, the same number of block partitions can be carried out on the disconnected point bitmap, and the number of corresponding block people is consistent, so that the optimal matching of the flood can be continuously realized in a recursion mode.
According to a specific implementation manner of the embodiment of the present invention, the hash distance is a mean hash distance. The step of calculating the average hash distance is as follows:
step 1: downscaling pictures to nxn, n in total2A plurality of pixels;
step 2: converting the nxn picture into a gray map;
and step 3: calculating the pixel average value of the gray level image;
and 4, step 4: traversing each pixel P in the gray-scale mapiAnd comparing the pixel value with the pixel average value if PiThe average value of the pixels is more than or equal to 1, otherwise, the average value is 0, and n is obtained2The binary string of each bit is the hash value of the picture mean value;
and 5: and calculating the Hamming distance of the hash values of the mean values of the two pictures, namely the hash distance of the mean values.
According to a specific implementation manner of the embodiment of the invention, the number of the levels of block division is 2-3 layers. Fig. 4 already shows the dynamic effect of the middle performance process under different hierarchical mapping strategies, and the influence of different hierarchical division on the later manual modification rate is large. The manual modification rate of the crowd dynamic relation mapping generated without space division is high. However, excessive hierarchical division may not only take more computation time but also cause deterioration in effect due to overfitting. The double-layer and three-layer models can obtain better working effect, so the models can be set as the optimized number of layers.
According to a specific implementation of the embodiment of the present invention, the threshold of each middle level is 5% of the number of people in the upper level, and the threshold of the last level is 1.
In another aspect, the present invention further provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a mass-crowd behavior-aided planning method as described above.
In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a large-scale crowd behavior aided planning method as described above.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform a method of assisted large-scale crowd behavior planning as described above.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not constitute a limitation on the element itself.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A large-scale crowd behavior aided planning method is characterized by comprising the following steps:
s1 designing crowd behavior effect videos;
s2 dynamic frame sampling is carried out on the video showing the crowd behavior effect, and the frame sampling intervalSI=min(I hash ,c) (ii) a Wherein C is a constant set by a user and represents the maximum sampling frame interval when the video content changes smoothly;I hash for dynamic frame spacing, it means that the hash distance between two sampled frames is not greater than a thresholdT hash The maximum frame interval of (a) is,T hash preset by a user;
s3, generating corresponding point locations according to each sampling frame obtained by sampling, and enabling the number of all the point locations of each image to be equal to the number NUM of individual crowds preset by a user;
s4, establishing a point location coordinate mapping relation between every two adjacent sampling frames;
s5, obtaining the coordinates and transformation of each crowd individual in the action process according to the time and point location coordinates corresponding to the sampling frames and the point location coordinate mapping relation among the sampling frames, thereby obtaining a crowd action scheme;
the method for establishing the point coordinate mapping relationship between every two adjacent sampling frames in step S4 includes:
s41, selecting seeds for neighborhood growth by using the same method for point bitmaps of two adjacent sampling frames, and controlling the number of individuals in a growth area through the same threshold value;
s42, converting the bitmap from a block set to a vertex set by taking the block center as the vertex position in the partitioned block neighborhood in the bitmap;
s43, establishing a complete bipartite graph by taking the Euclidean distance of the block vertexes between the converted point bitmaps as the edge weight, and realizing the optimal matching among the block sets in the complete bipartite graph through a minimum weight matching algorithm;
s44, reducing the threshold standard of region growing, repeating the steps S41-S44 for all matched sub-blocks to carry out next-level block division until each block only contains one individual;
and S45, obtaining the point location coordinate mapping relation between two adjacent sampling frames according to the matching result of the last layer.
2. The large-scale crowd behavior aided planning method according to claim 1, wherein when the seeds are selected for neighborhood growth, the seeds are selected from the edge to the middle of the crowd connected domain for neighborhood growth, and if the neighborhood growth process is not connected, the neighborhood search range is expanded.
3. The mass-population behavior aided planning method of claim 2, wherein the hash distance is a mean hash distance.
4. The large-scale crowd behavior aided planning method of claim 3, wherein the number of the levels of block division is 2-3.
5. The mass-production crowd behavior aided planning method of claim 4, wherein the threshold value of each middle level is 5% of the number of people in the upper region, and the threshold value of the last layer is 1.
6. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of assisted mass population behaviour planning as claimed in any one of claims 1 to 5.
7. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of assisted large-scale crowd behavior planning of any of claims 1-5.
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