CN111444758A - Pedestrian re-identification method and device based on spatio-temporal information - Google Patents
Pedestrian re-identification method and device based on spatio-temporal information Download PDFInfo
- Publication number
- CN111444758A CN111444758A CN201911368354.0A CN201911368354A CN111444758A CN 111444758 A CN111444758 A CN 111444758A CN 201911368354 A CN201911368354 A CN 201911368354A CN 111444758 A CN111444758 A CN 111444758A
- Authority
- CN
- China
- Prior art keywords
- image set
- identified
- pedestrian
- target
- space
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/103—Static body considered as a whole, e.g. static pedestrian or occupant recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Human Computer Interaction (AREA)
- Multimedia (AREA)
- Traffic Control Systems (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a pedestrian re-identification method and a device based on spatio-temporal information, wherein the pedestrian re-identification method based on the spatio-temporal information comprises the following steps: acquiring an image set to be identified; obtaining a target image set to be identified according to the image set to be identified; extracting target features of the target image set to be recognized according to a preset convolutional neural network model, wherein the target features comprise time features and space features; obtaining the cosine distance of the target feature, and determining the similarity of the target feature according to the cosine distance; and determining a pedestrian re-identification result according to the similarity and the space-time probability. According to the method and the device, the time information and the space information of the pedestrian image are extracted, and the pedestrian is identified according to the time information and the space information, so that the accuracy of video pedestrian re-identification is improved.
Description
Technical Field
The invention relates to the technical field of model construction, in particular to a pedestrian re-identification method and device based on spatio-temporal information.
Background
The pedestrian re-identification is a technology for identifying the identity of a pedestrian under different camera scenes, is a very important part in a video monitoring analysis technology, and plays an important role in the aspects of intelligent video monitoring, crime prevention, social security maintenance and the like.
In recent years, a deep learning method is widely applied to a plurality of computer vision fields such as image classification and target identification, and compared with a traditional manual design method, the deep learning method can obtain better performance, however, due to the fact that a monitoring video is complex, the monitoring video is affected by factors such as illumination, weather, visual angle transformation and pedestrian posture which are changed violently, and the resolution of imaging equipment is poor, the same pedestrian is difficult to identify under different cameras, and the accuracy of pedestrian re-identification is low.
Disclosure of Invention
The invention aims to overcome the defect of low accuracy rate of pedestrian re-identification in the prior art, and provides a pedestrian re-identification method and device based on a space-time model.
According to a first aspect, the embodiment of the invention discloses a pedestrian re-identification method based on spatiotemporal information, which comprises the following steps: acquiring an image set to be identified; obtaining a target image set to be identified according to the image set to be identified; extracting target features of the target image set to be recognized according to a preset convolutional neural network model, wherein the target features comprise time features and space features; obtaining the cosine distance of the target feature, and determining the similarity of the target feature according to the cosine distance; and determining a pedestrian re-identification result according to the similarity and the space-time probability.
With reference to the first aspect, in a first implementation manner of the first aspect, the obtaining a target image set to be recognized according to the image set to be recognized includes: carrying out pedestrian detection on the image set to be identified to obtain a first image set to be identified; and screening the first image set to be identified to obtain the target identification image set.
With reference to the first embodiment of the first aspect, in a second embodiment of the first aspect, the spatio-temporal probability is determined by: acquiring time information and space information of a pedestrian image; obtaining a migration time difference according to the time information and the space information; and obtaining the space-time probability according to the migration time difference.
With reference to the first aspect, in a third implementation manner of the first aspect, the method further includes: acquiring a training image set; and training the neural network model according to the training image set to obtain a convolutional neural network model.
According to a second aspect, an embodiment of the present invention further discloses a pedestrian re-identification apparatus based on spatiotemporal information, including: the first acquisition module is used for acquiring an image set to be identified; the target image set to be identified acquisition module is used for acquiring a target image set to be identified according to the image set to be identified; the extraction module is used for extracting target features of the target image set to be recognized according to a preset convolutional neural network model, wherein the target features comprise time features and space features; the second acquisition module is used for acquiring the cosine distance of the target feature and determining the similarity of the target feature according to the cosine distance; and the determining module is used for determining a pedestrian re-identification result according to the similarity and the space-time probability.
With reference to the second aspect, in a first implementation manner of the second aspect, the target image set to be recognized acquisition module includes: the detection module is used for carrying out pedestrian detection on the image set to be identified to obtain a first image set to be identified; and the screening module is used for screening the first image set to be identified to obtain the target identification image set.
With reference to the first embodiment of the second aspect, in a second embodiment of the second aspect, the apparatus further comprises: the third acquisition module is used for acquiring time information and space information of the pedestrian image; the migration time difference acquisition module is used for acquiring a migration time difference according to the time information and the space information; and the space-time probability obtaining module is used for obtaining the space-time probability according to the migration time difference.
With reference to the second aspect, in a third embodiment of the second aspect, the apparatus further comprises: the fourth acquisition module is used for acquiring a training image set; and the training module is used for training the neural network model according to the training image set to obtain a convolutional neural network model.
According to a third aspect, an embodiment of the present invention further discloses an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the one processor to cause the at least one processor to perform the method for pedestrian re-identification based on spatiotemporal information as defined in the first aspect or any one of the embodiments of the first aspect.
According to a fourth aspect, the embodiment of the present invention further discloses a computer-readable storage medium, on which computer instructions are stored, and the computer instructions, when executed by a processor, implement the pedestrian re-identification method based on spatiotemporal information as described in the first aspect or any one of the embodiments of the first aspect.
The technical scheme of the invention has the following advantages:
according to the pedestrian re-identification method and device based on the spatio-temporal information, the image set to be identified is obtained according to the image set to be identified, the target characteristics of the image set to be identified are extracted according to the preset convolutional neural network model, the target characteristics comprise time characteristics and space characteristics, the cosine distance of the target characteristics is obtained, the similarity of the target characteristics is determined according to the cosine distance, and the pedestrian re-identification result is determined according to the similarity and the spatio-temporal probability. According to the method and the device, the time information and the space information of the pedestrian image are extracted, and the pedestrian is identified according to the time information and the space information, so that the accuracy of video pedestrian re-identification is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of a pedestrian re-identification method based on spatiotemporal information in embodiment 1 of the present invention;
fig. 2 is a schematic block diagram of a specific example of a pedestrian re-identification apparatus based on spatiotemporal information according to embodiment 2 of the present invention;
fig. 3 is a diagram of an embodiment of an electronic terminal in embodiment 3 of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The embodiment of the invention provides a pedestrian re-identification method based on spatio-temporal information, which is applied to an electronic purse net monitoring video and comprises the following steps as shown in figure 1:
s11: and acquiring an image set to be identified.
Illustratively, the image set to be identified may be a surveillance video acquired by an electronic purse net across a camera device, and the image set to be identified may be directly uploaded to a terminal by a user in a wired or wireless manner, or may be obtained by logging in the terminal. The acquisition mode of the image to be recognized is not particularly limited in the embodiment of the application, and a person skilled in the art can determine the acquisition mode according to actual use needs.
S12: and obtaining a target image set to be identified according to the image set to be identified.
For example, the target image set to be recognized may be regarded as an unobstructed and clear image set including a pedestrian, and the pedestrian detection may be implemented in a manual labeling manner or an automatic labeling manner.
S13: and extracting target features according to a preset convolutional neural network model, wherein the target features comprise time features and space features.
Illustratively, the visual features of a target image set to be recognized are extracted according to a preset convolutional neural network model, the target image set to be recognized has a plurality of space-time points, the spatial features are camera numbers, and the temporal features are time differences shot between every two image sets to be recognized.
S14: and obtaining the cosine distance of the target feature, and determining the similarity of the target feature according to the cosine distance.
Exemplarily, the similarity refers to the similarity between different target features, and the greater the cosine distance, the smaller the similarity of the target features; the smaller the cosine distance, the greater the similarity of the target features. Cosine similarity refers to a cosine value between included angles of two vectors in a vector space and is used for measuring the difference between two target characteristics, the cosine value is close to 1, the included angle tends to 0, the more similar the two target characteristic vectors are, the cosine value is close to 0, the included angle tends to 90 degrees, and the more dissimilar the two target characteristic vectors are.
S15: and determining a pedestrian re-identification result according to the similarity and the space-time probability.
Illustratively, the pedestrian re-identification accuracy rate is calculated according to the similarity of any two images judged by the image classifier in the preset convolutional neural network and the estimated spatio-temporal probability, and specifically can be calculated according to the following formula:
the pedestrian re-identification method based on the spatio-temporal information obtains an image set to be identified by obtaining the image set to be identified, obtains a target image set to be identified according to the image set to be identified, extracts target characteristics of the target image set to be identified according to a preset convolutional neural network model, obtains cosine distances of the target characteristics, determines similarity of the target characteristics according to the cosine distances, and determines a pedestrian re-identification result according to the similarity and the spatio-temporal probability. According to the method and the device, the time information and the space information of the pedestrian image are extracted, and the pedestrian is identified according to the time information and the space information, so that the accuracy of video pedestrian re-identification is improved.
As an optional embodiment of the present application, obtaining a target image set to be recognized according to an image set to be recognized includes:
firstly, pedestrian detection is carried out on an image set to be recognized, and a first image set to be recognized is obtained.
For example, a video shot by the camera device may be divided into one frame and one frame of video frames, the first image to be identified refers to a video frame accurately marked by a pedestrian, the video frame is subjected to pedestrian detection through a pedestrian detection algorithm, and the pedestrian detection may be realized through a manual marking or an automatic marking manner. The embodiment of the invention does not limit the pedestrian detection algorithm which is automatically marked, and the person skilled in the art can select the algorithm according to the actual situation.
And secondly, screening the first image set to be identified to obtain a target identification image set.
For example, the marked video frames are screened to obtain clear and non-occluded video frames, the application may regard an occlusion of less than or equal to 10% of pedestrians as no occlusion, and may regard an image with pixels greater than or equal to 1280 × 720 as a clear image. The standard of non-occlusion and clearness is not specifically limited in the embodiments of the present application, and those skilled in the art can determine the standard according to actual use requirements.
As an optional embodiment of the present application, the pedestrian re-identification method based on spatiotemporal information further includes:
first, time information and spatial information of a pedestrian image are acquired.
Illustratively, taking an image of a pedestrian in mark 1501 as an example, the time information and the space information of the image of the pedestrian are written in the name of the picture, for example, the name of the image may be 0007-c3-077419.jpg, where 0007 represents the identity of the target person, c3 represents the identity of the target person captured by camera No. 3, that is, the space information, and 077419 represents the frame number, that is, the time information of the pedestrian. The space-time information is very easy to store, and the space-time information can be recorded and effectively utilized as long as the time when the picture is shot and which camera is shot is known.
And secondly, obtaining the migration time difference according to the time information and the space information.
Illustratively, in a spatiotemporal model, that is, in a camera network, a pedestrian calculates a migration time difference according to two spatiotemporal points corresponding to two images given the distribution of the migration time between two cameras, and counts all the migration time differences in a data set.
And thirdly, obtaining the space-time probability according to the migration time difference.
For example, in the embodiment of the present application, the occurrence frequency of the migration time difference in a certain range before and after the migration time difference is calculated, for example, the occurrence frequency may be the number of the migration time differences in the target range/the total number of the migration time differences, as a probability (instantaneous null probability) that a new time difference occurs, that is, a probability that two spatio-temporal points are generated by the same person, the certain range may be 100 frames or 200 frames, the range is not limited in the embodiment of the present application, and a person skilled in the art may set the range according to actual situations.
As an optional embodiment of the present application, the pedestrian re-identification method based on spatiotemporal information further includes:
first, a training image set is acquired.
Illustratively, in order to improve the accuracy of pedestrian re-identification, the selection of the training image set should be various, and may include, for example, pedestrian posture diversity, pedestrian scale diversity, background diversity, etc., the training image set may employ DukeMTMC-reID, CUHK03 or mark-1501, and the image set may be represented by 7: 3, taking the image set with the larger proportion of 70% as a training image set, and taking the image set with the smaller proportion of 30% as a verification image set, wherein the verification image set can be used for verifying the pedestrian re-identification model. The training image set is not particularly limited by the embodiment of the present invention, and those skilled in the art can select the training image set according to actual situations.
Secondly, training the neural network model according to the training image set to obtain a convolution neural network model.
Illustratively, a training image set is input into a neural network model, the weight of the neural network model is continuously adjusted according to the training image set, supervised training is carried out to obtain a convolutional neural network model, then the convolutional neural network model is verified, the convolutional neural network can be verified by adopting the verification image set, a Rank evaluation system can be adopted in the method, Rank-1 is a first-order hit rate, namely whether the graph ranked at the first order hits the user himself or herself, Rank-n indicates the probability that the highest n graphs in the search results have correct results, Rank-5 can be adopted in the embodiment of the method, and 4 correct graphs in the highest 5 graphs in the search results serve as evaluation. The evaluation system is not limited in the embodiment of the invention, and can be set by a person skilled in the art according to actual conditions.
Example 2
The embodiment of the invention also provides a pedestrian re-identification device based on the spatio-temporal information, which is applied to the monitoring video of the electronic purse net and comprises the following components as shown in figure 2:
a first obtaining module 21, configured to obtain an image set to be identified; the specific implementation manner is shown in step S11 in embodiment 1, and details are not described here.
The target image set to be identified acquisition module 22 is configured to obtain a target image set to be identified according to the image set to be identified; the specific implementation manner is shown in step S12 in embodiment 1, and details are not described here.
The extraction module 23 is configured to extract target features of the target image set to be recognized according to a preset convolutional neural network model, where the target features include temporal features and spatial features; the specific implementation manner is shown in step S13 in embodiment 1, and details are not described here.
The second obtaining module 24 is configured to obtain a cosine distance of the target feature, and determine a similarity of the target feature according to the cosine distance; the specific implementation manner is shown in step S14 in embodiment 1, and details are not described here.
And the determining module 25 is used for determining the pedestrian re-identification result according to the similarity and the space-time probability. The specific implementation manner is shown in step S15 in embodiment 1, and details are not described here.
The pedestrian re-identification device based on the spatio-temporal information obtains an image set to be identified by obtaining the image set to be identified, obtains a target image set to be identified according to the image set to be identified, extracts target characteristics of the target image set to be identified according to a preset convolutional neural network model, obtains cosine distances of the target characteristics, calculates similarity of the target characteristics according to the cosine distances, and determines a pedestrian re-identification result according to the similarity and the spatio-temporal probability. According to the method and the device, the time information and the space information of the pedestrian image are extracted, and the pedestrian is identified according to the time information and the space information, so that the accuracy of video pedestrian re-identification is improved.
As an optional implementation manner of the application, the target image set to be recognized acquisition module comprises:
the detection module is used for detecting pedestrians in the image set to be recognized to obtain a first image set to be recognized; the specific implementation manner is shown in the corresponding steps in embodiment 1, and is not described herein again.
And the screening module is used for screening the first image set to be identified to obtain a target identification image set. The specific implementation manner is shown in the corresponding steps in embodiment 1, and is not described herein again.
As an optional embodiment of the present application, the pedestrian re-identification apparatus based on spatiotemporal information further includes:
the third acquisition module is used for acquiring time information and space information of the pedestrian image; the specific implementation manner is shown in the corresponding steps in embodiment 1, and is not described herein again.
The migration time difference acquisition module is used for acquiring a migration time difference according to the time information and the space information; the specific implementation manner is shown in the corresponding steps in embodiment 1, and is not described herein again.
And the space-time probability obtaining module is used for obtaining the space-time probability according to the migration time difference. The specific implementation manner is shown in the corresponding steps in embodiment 1, and is not described herein again.
As an optional embodiment of the present application, the pedestrian re-identification apparatus based on spatiotemporal information further includes:
the fourth acquisition module is used for acquiring a training image set; the specific implementation manner is shown in the corresponding steps in embodiment 1, and is not described herein again.
And the training module is used for training the neural network model according to the training image set to obtain a convolutional neural network model. The specific implementation manner is shown in the corresponding steps in embodiment 1, and is not described herein again.
Example 3
An embodiment of the present invention further provides an electronic device, as shown in fig. 3, the electronic device may include a processor 31 and a memory 32, where the processor 31 and the memory 32 may be connected by a bus or in another manner, and fig. 3 takes the connection by the bus as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 32 is a non-transitory computer-readable storage medium, and can be used for storing non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the pedestrian re-identification method based on spatiotemporal information in embodiment 1 of the present invention (for example, the first acquisition module 21, the target to-be-identified image set acquisition module 22, the extraction module 23, the second acquisition module 25, and the determination module 25 shown in fig. 2). The processor 31 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 32, namely, implements the pedestrian re-identification method based on spatiotemporal information in the above method embodiments.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 31, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 32, and when executed by the processor 31, perform the pedestrian re-identification method based on spatiotemporal information as in embodiment 1 shown in fig. 1.
The details of the electronic device may be understood with reference to the corresponding related description and effects in the embodiment shown in fig. 1, and are not described herein again.
Example 4
The embodiment of the invention also provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the pedestrian re-identification method based on the spatiotemporal information in the embodiment 1. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Memory Access (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A pedestrian re-identification method based on spatio-temporal information is characterized by comprising the following steps:
acquiring an image set to be identified;
obtaining a target image set to be identified according to the image set to be identified;
extracting target features of the target image set to be recognized according to a preset convolutional neural network model, wherein the target features comprise time features and space features;
obtaining the cosine distance of the target feature, and determining the similarity of the target feature according to the cosine distance;
and determining a pedestrian re-identification result according to the similarity and the space-time probability.
2. The method of claim 1, wherein obtaining a target image set to be identified from the image set to be identified comprises:
carrying out pedestrian detection on the image set to be identified to obtain a first image set to be identified;
and screening the first image set to be identified to obtain the target identification image set.
3. The method of claim 2, wherein the spatio-temporal probabilities are determined by:
acquiring time information and space information of a pedestrian image;
obtaining a migration time difference according to the time information and the space information;
and obtaining the space-time probability according to the migration time difference.
4. The method of claim 1, further comprising:
acquiring a training image set;
and training the neural network model according to the training image set to obtain a convolutional neural network model.
5. A pedestrian re-identification device based on spatiotemporal information, characterized by comprising:
the first acquisition module is used for acquiring an image set to be identified;
the target image set to be identified acquisition module is used for acquiring a target image set to be identified according to the image set to be identified;
the extraction module is used for extracting target features of the target image set to be recognized according to a preset convolutional neural network model, wherein the target features comprise time features and space features;
the second acquisition module is used for acquiring the cosine distance of the target feature and determining the similarity of the target feature according to the cosine distance;
and the determining module is used for determining a pedestrian re-identification result according to the similarity and the space-time probability.
6. The apparatus of claim 5, wherein the target image set to be identified acquisition module comprises:
the detection module is used for carrying out pedestrian detection on the image set to be identified to obtain a first image set to be identified;
and the screening module is used for screening the first image set to be identified to obtain the target identification image set.
7. The apparatus of claim 6, further comprising:
the third acquisition module is used for acquiring time information and space information of the pedestrian image;
the migration time difference acquisition module is used for acquiring a migration time difference according to the time information and the space information;
and the space-time probability obtaining module is used for obtaining the space-time probability according to the migration time difference.
8. The apparatus of claim 5, further comprising:
the fourth acquisition module is used for acquiring a training image set;
and the training module is used for training the neural network model according to the training image set to obtain a convolutional neural network model.
9. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of pedestrian re-identification based on spatiotemporal information as defined in any one of claims 1 to 4.
10. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the method for pedestrian re-identification based on spatiotemporal information according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911368354.0A CN111444758A (en) | 2019-12-26 | 2019-12-26 | Pedestrian re-identification method and device based on spatio-temporal information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911368354.0A CN111444758A (en) | 2019-12-26 | 2019-12-26 | Pedestrian re-identification method and device based on spatio-temporal information |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111444758A true CN111444758A (en) | 2020-07-24 |
Family
ID=71648773
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911368354.0A Pending CN111444758A (en) | 2019-12-26 | 2019-12-26 | Pedestrian re-identification method and device based on spatio-temporal information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111444758A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381024A (en) * | 2020-11-20 | 2021-02-19 | 华南理工大学 | Multi-mode fused unsupervised pedestrian re-identification and rearrangement method |
| CN113420679A (en) * | 2021-06-26 | 2021-09-21 | 南京搜文信息技术有限公司 | Artificial intelligent cross-camera multi-target tracking system and tracking algorithm |
| CN113780172A (en) * | 2021-09-10 | 2021-12-10 | 济南博观智能科技有限公司 | Pedestrian re-identification method, device, equipment and storage medium |
| CN116052220A (en) * | 2023-02-07 | 2023-05-02 | 北京多维视通技术有限公司 | Pedestrian re-identification method, device, equipment and medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105389562A (en) * | 2015-11-13 | 2016-03-09 | 武汉大学 | Secondary optimization method for monitoring video pedestrian re-identification result based on space-time constraint |
| CN107133575A (en) * | 2017-04-13 | 2017-09-05 | 中原智慧城市设计研究院有限公司 | A kind of monitor video pedestrian recognition methods again based on space-time characteristic |
| CN107545256A (en) * | 2017-09-29 | 2018-01-05 | 上海交通大学 | A kind of camera network pedestrian recognition methods again of combination space-time and network consistency |
| CN109325471A (en) * | 2018-10-31 | 2019-02-12 | 中山大学 | A kind of binary-flow network pedestrian recognition methods again of combination appearance features and spatial and temporal distributions |
| CN109446898A (en) * | 2018-09-20 | 2019-03-08 | 暨南大学 | A kind of recognition methods again of the pedestrian based on transfer learning and Fusion Features |
| CN109657533A (en) * | 2018-10-27 | 2019-04-19 | 深圳市华尊科技股份有限公司 | Pedestrian recognition methods and Related product again |
-
2019
- 2019-12-26 CN CN201911368354.0A patent/CN111444758A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105389562A (en) * | 2015-11-13 | 2016-03-09 | 武汉大学 | Secondary optimization method for monitoring video pedestrian re-identification result based on space-time constraint |
| CN107133575A (en) * | 2017-04-13 | 2017-09-05 | 中原智慧城市设计研究院有限公司 | A kind of monitor video pedestrian recognition methods again based on space-time characteristic |
| CN107545256A (en) * | 2017-09-29 | 2018-01-05 | 上海交通大学 | A kind of camera network pedestrian recognition methods again of combination space-time and network consistency |
| CN109446898A (en) * | 2018-09-20 | 2019-03-08 | 暨南大学 | A kind of recognition methods again of the pedestrian based on transfer learning and Fusion Features |
| CN109657533A (en) * | 2018-10-27 | 2019-04-19 | 深圳市华尊科技股份有限公司 | Pedestrian recognition methods and Related product again |
| CN109325471A (en) * | 2018-10-31 | 2019-02-12 | 中山大学 | A kind of binary-flow network pedestrian recognition methods again of combination appearance features and spatial and temporal distributions |
Non-Patent Citations (1)
| Title |
|---|
| 梦里风林: "CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns", 《HTTPS://WWW.CNBLOGS.COM/HELLOCWH/P/8616371.HTML》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381024A (en) * | 2020-11-20 | 2021-02-19 | 华南理工大学 | Multi-mode fused unsupervised pedestrian re-identification and rearrangement method |
| CN112381024B (en) * | 2020-11-20 | 2023-06-23 | 华南理工大学 | Multi-mode-fused unsupervised pedestrian re-identification rearrangement method |
| CN113420679A (en) * | 2021-06-26 | 2021-09-21 | 南京搜文信息技术有限公司 | Artificial intelligent cross-camera multi-target tracking system and tracking algorithm |
| CN113420679B (en) * | 2021-06-26 | 2024-04-26 | 南京搜文信息技术有限公司 | Artificial intelligence cross-camera multi-target tracking system and tracking method |
| CN113780172A (en) * | 2021-09-10 | 2021-12-10 | 济南博观智能科技有限公司 | Pedestrian re-identification method, device, equipment and storage medium |
| CN113780172B (en) * | 2021-09-10 | 2024-01-23 | 济南博观智能科技有限公司 | Pedestrian re-identification method, device, equipment and storage medium |
| CN116052220A (en) * | 2023-02-07 | 2023-05-02 | 北京多维视通技术有限公司 | Pedestrian re-identification method, device, equipment and medium |
| CN116052220B (en) * | 2023-02-07 | 2023-11-24 | 北京多维视通技术有限公司 | Pedestrian re-identification method, device, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10896323B2 (en) | Method and device for image processing, computer readable storage medium, and electronic device | |
| CN111444758A (en) | Pedestrian re-identification method and device based on spatio-temporal information | |
| CN109325964B (en) | Face tracking method and device and terminal | |
| US20170213080A1 (en) | Methods and systems for automatically and accurately detecting human bodies in videos and/or images | |
| US8977060B2 (en) | Motion stabilization and detection of articulated objects | |
| US20130162867A1 (en) | Method and system for robust scene modelling in an image sequence | |
| CN111723773B (en) | Method and device for detecting carryover, electronic equipment and readable storage medium | |
| CN111163259A (en) | Image capturing method, monitoring camera and monitoring system | |
| JP6654789B2 (en) | Apparatus, program, and method for tracking object considering multiple candidates at change points | |
| CN110059666B (en) | Attention detection method and device | |
| CN111435437A (en) | PCB pedestrian re-recognition model training method and PCB pedestrian re-recognition method | |
| CN111666800A (en) | Pedestrian re-recognition model training method and pedestrian re-recognition method | |
| US20220366570A1 (en) | Object tracking device and object tracking method | |
| CN111354024B (en) | Behavior prediction method of key target, AI server and storage medium | |
| CN110569770A (en) | Human body intrusion behavior recognition method and device, storage medium and electronic equipment | |
| KR20190047748A (en) | Method for object of image and apparatus for the same | |
| CN111738043A (en) | Pedestrian re-identification method and device | |
| JP7243372B2 (en) | Object tracking device and object tracking method | |
| US10916016B2 (en) | Image processing apparatus and method and monitoring system | |
| CN113243015B (en) | Video monitoring system | |
| CN111753775A (en) | Fish growth assessment method, device, equipment and storage medium | |
| CN112927258A (en) | Target tracking method and device | |
| CN112990156B (en) | Optimal target capturing method and device based on video and related equipment | |
| KR101154350B1 (en) | Multiple Images Processing system using Detection of Objects and segmenting - recognizing of High- resolution Image and there of. | |
| CN113743313A (en) | Pedestrian identification method and device and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200724 |