WO2020220951A1 - 一种录像数据存储方法及装置 - Google Patents
一种录像数据存储方法及装置 Download PDFInfo
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- WO2020220951A1 WO2020220951A1 PCT/CN2020/083732 CN2020083732W WO2020220951A1 WO 2020220951 A1 WO2020220951 A1 WO 2020220951A1 CN 2020083732 W CN2020083732 W CN 2020083732W WO 2020220951 A1 WO2020220951 A1 WO 2020220951A1
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- 238000000034 method Methods 0.000 title claims abstract description 81
- 238000013500 data storage Methods 0.000 title claims abstract description 29
- 238000013136 deep learning model Methods 0.000 claims description 15
- 238000007781 pre-processing Methods 0.000 claims description 14
- 230000008569 process Effects 0.000 description 10
- 238000012544 monitoring process Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
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- 238000004891 communication Methods 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000003139 buffering effect Effects 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
- H04N5/78—Television signal recording using magnetic recording
- H04N5/781—Television signal recording using magnetic recording on disks or drums
Definitions
- This application relates to the field of monitoring technology, in particular to a method and device for storing video data.
- the video capture device collects the video data, it needs to send the video data to electronic equipment such as DVR (Digital Video Recorder), NVR (Network Video Recorder), central storage device, etc. storage.
- electronic equipment such as DVR (Digital Video Recorder), NVR (Network Video Recorder), central storage device, etc. storage.
- DVR Digital Video Recorder
- NVR Network Video Recorder
- central storage device etc. storage.
- the electronic device directly stores the video data after receiving the video data.
- monitoring channels such as IPC (Internet Protocol Camera) channels and analog channels under a monitoring system
- IPC Internet Protocol Camera
- analog channels under a monitoring system
- the purpose of the embodiments of the present application is to provide a method and device for storing video data, so as to save the hardware cost of the electronic device storing video data.
- the specific technical solutions are as follows:
- an embodiment of the present application provides a recording data storage method, which includes:
- the recording data of this channel will be stored.
- the recording data includes audio data
- the steps of performing target recognition on the recording data of the channel and identifying the designated target in the recording data of the channel include:
- the recording data includes video data
- the steps of performing target recognition on the recording data of the channel and identifying the designated target in the recording data of the channel include:
- each image data to be recognized it is determined whether there is a designated target in the video data of the channel.
- the method further includes:
- the step of storing the recording data of the channel includes:
- an embodiment of the present application provides a video data storage device, which includes:
- the acquisition module is used to sequentially acquire the recording data of each channel in a polling manner
- the recognition module is used to identify the target of the recording data of a channel after acquiring the recording data of a channel each time, and identify the designated target in the recording data of the channel;
- the storage module is used to store the video data of the channel if there is a designated target in the video data of the channel.
- the recording data includes audio data
- Identification module specifically used for:
- the recording data includes video data
- Identification module specifically used for:
- each image data to be recognized it is determined whether there is a designated target in the video data of the channel.
- the device further includes:
- the stop module is used to stop the storage of the channel's video data if there is no designated target in the channel's video data.
- storage module specifically used for:
- an embodiment of the present application provides an electronic device including a processor and a memory, where the memory stores machine executable instructions that can be executed by the processor, and the machine executable instructions are loaded and executed by the processor to achieve The method provided in the first aspect of the embodiments of the present application.
- an embodiment of the present application provides a machine-readable storage medium, and the machine-readable storage medium stores machine-executable instructions.
- the machine-executable instructions When the machine-executable instructions are loaded and executed by a processor, they implement the first On the one hand the method provided.
- an embodiment of the present application provides an application program for execution at runtime: the method provided in the first aspect of the embodiment of the present application.
- the video data storage method and device acquire the video data of each channel in turn in a polling manner, and perform target identification on the video data of the channel after acquiring the video data of a channel each time .
- To identify the specified target in the recording data of the channel if the specified target exists in the recording data of the channel, the recording data of the channel will be stored.
- Target recognition is performed on the video data collected by a channel, and it is judged whether there is a specified target in the video data of the channel. If it exists, the video data of the channel is stored, and the above operations are performed on each channel by polling to ensure
- the electronic device stores the video data collected by each channel with the specified target.
- the amount of stored video data is less than the amount of data stored in the traditional way, and the storage space required is less than the storage space required by the traditional way, saving the storage of video
- the hardware cost of the data electronic device is less than the amount of data stored in the traditional way, and the storage space required is less than the storage space required by the traditional way, saving the storage of
- FIG. 1 is a schematic flowchart of a method for storing video data according to an embodiment of the application
- FIG. 2 is a schematic flowchart of a recording data storage method according to another embodiment of the application.
- FIG. 3 is a schematic structural diagram of an electronic device in an application scenario of an embodiment of the application.
- FIG. 4 is a schematic flowchart of a method for storing video data executed by a DVR according to an embodiment of the application
- FIG. 5 is a schematic flowchart of a video data storage method executed by an NVR according to an embodiment of the application
- FIG. 6 is a schematic diagram of the structure of a video data storage device according to an embodiment of the application.
- FIG. 7 is a schematic structural diagram of an electronic device according to an embodiment of the application.
- embodiments of the present application provide a video data storage method, device, electronic device, and machine-readable storage medium.
- the video data storage method provided by the embodiment of the present application is first introduced.
- the video data storage method provided by the embodiments of the present application can be applied to electronic devices with video data storage functions such as DVR, NVR, and central storage devices.
- the method for implementing the recording data storage method provided in the embodiments of the present application may be at least one of software, hardware circuit, and logic circuit provided in the above electronic device.
- the method for storing video data may include the following steps.
- S101 Obtain video data of each channel in sequence according to a polling manner.
- the monitoring system includes channels for collecting video data such as IPC channels and analog channels. Each channel is responsible for collecting video data within a certain monitoring range. Each channel sends the collected video data to the electronic device, and the electronic device stores the video data .
- the electronic device obtains the recording data of each channel in turn according to the polling method.
- the polling method is to first obtain the recording data of one channel, and after an interval of several frames or a period of time, obtain the recording data of the next channel.
- the polling sequence for each channel can be preset, and the polling time interval can also be preset. Normally, the polling time interval can be set according to the time required for subsequent target identification, for example, The time for one target identification is 20ms, and the set polling interval can be greater than or equal to 20ms.
- S102 After acquiring the video recording data of a channel each time, perform target recognition on the video recording data of the channel, and identify the designated target in the video recording data of the channel.
- the electronic device After acquiring the video data of a channel, the electronic device performs target recognition on the video data of the channel, and recognizes the car (vehicle brand, car model, license plate, etc. attributes) that the user cares about, and people (men, women, top color, bottom clothes) Color, whether to ride a bicycle or other attributes) and other designated targets, determine whether there is a designated target in the video data of the channel.
- car vehicle brand, car model, license plate, etc. attributes
- people men, women, top color, bottom clothes
- Color whether to ride a bicycle or other attributes
- the video data of the channel can be stored.
- the recording data of each channel is obtained in turn according to the polling method.
- the recording data of a channel is identified by the target, and the recording data of the channel is identified Specify target, if there is a specified target in the recording data of this channel, the recording data of this channel will be stored.
- Target recognition is performed on the video data collected by a channel, and it is judged whether there is a specified target in the video data of the channel. If it exists, the video data of the channel is stored, and the above operations are performed on each channel by polling to ensure The electronic device stores the video data collected by each channel with the specified target.
- the amount of stored video data is less than the amount of data stored in the traditional way, and the storage space required is less than the storage space required by the traditional way, saving the storage of video
- the hardware cost of the data electronic device is adopted to reduce the demand for computing resources, and at the same time, it can achieve the purpose of target detection in all channels.
- the recording data may include audio data.
- S102 can be specifically implemented by the following steps: preprocessing the audio data of the channel to obtain the audio data to be identified; using the sliding window method to obtain audio units in different time domains from the audio data to be identified; using the first Preset the deep learning model, perform audio recognition on each audio unit, and obtain the recognition result of each audio unit; use the pre-built language model library to match the recognition results of each audio unit with similarity; according to the matching result of each audio unit To determine whether there is a specified target in the audio data of the channel.
- the process of preprocessing audio data can be to set sampling parameters such as audio sampling rate, bit width, etc., regularize the audio sampling, and also use noise filtering to filter out the noise.
- the obtained audio data to be identified is Noise-free regular audio data.
- a sliding window method can be used to obtain audio units in different time domains from the audio data to be recognized.
- RNN Recurrent Neural Network, cyclic neural network
- other first preset deep learning models perform voice recognition on each audio unit, and obtain the recognition result of each audio unit.
- the recognition result is the probability of what the audio content in the audio unit is, generally on electronic devices
- a language model library is established in advance.
- the language model library stores audio type, content and other information. Using the language model library to match the similarity of the recognition results of each audio unit, you can determine the audio collected by one channel Whether there is a designated target in the data, the higher the matching degree, the greater the possibility of the designated target in the audio data.
- the recording data may include video data.
- S102 can be specifically implemented through the following steps: preprocessing each image data in the video data of the channel to obtain each image data to be recognized; using the second preset deep learning model to perform Target recognition: According to the recognition result of each image data to be recognized, it is judged whether there is a designated target in the video data of the channel.
- the process of preprocessing each image data in the video data is mainly to uniformly input the image data of the preset deep learning model, for example, the resolution and image color space can be unified, and the filtering technology can also be used to filter out the noise signal in the image , Using a second preset deep learning model such as FRCNN (Fast Region-based Convolutional Neural Network, fast convolutional neural network based on candidate regions) to perform target recognition on each image to be recognized, and the recognition results are obtained.
- FRCNN Fast convolutional neural network based on candidate regions
- the recognition of the specified target can only recognize the audio target, or only the video target, or both the audio target and the video target, which is not limited here.
- audio target recognition and video target recognition in addition to the above-mentioned deep neural network methods such as RNN and FRCNN, methods such as feature comparison and pixel matching can also be used, which will not be repeated here.
- S103 can be specifically implemented through the following steps:
- the recording data of the channel If there is a specified target in the recording data of the channel, read the recording data in the preset time period from the buffer corresponding to the channel, where the buffer area corresponding to the channel stores the preset time period before the current time
- the video data collected by the channel; the video data within the preset time period and the video data acquired in the channel are stored.
- the electronic device can open up a buffer for Cache the video data collected by the channel in the preset time period before the current time.
- the preset time period can be set according to the time it takes for the electronic device to complete a complete polling process for all channels, for example, a total of 5 If the polling interval for each channel is 20ms, the preset time period can be set to a time period greater than or equal to 100ms. In this way, it is equivalent to setting up the pre-recording function.
- the embodiment of the present application also provides a recording data storage method, as shown in FIG. 2, which may include the following steps.
- S201 Obtain video data of each channel in sequence according to a polling manner.
- S202 After acquiring the video recording data of a channel each time, perform target recognition on the video recording data of the channel, and identify the designated target in the video recording data of the channel.
- S201-S203 are the same as S101-S103 in the embodiment shown in FIG. 1, and will not be repeated here.
- the steps of S201-S203 need to be executed in a loop. If for a certain channel, during a certain recognition, it is recognized that the specified target does not exist in the recording data of the channel , You need to stop storing the video data of this channel.
- the recording data of each channel is obtained in turn according to the polling method.
- the recording data of a channel is identified by the target, and the recording data of the channel is identified Specify target, if there is a specified target in the recording data of this channel, the recording data of this channel will be stored.
- Target recognition is performed on the video data collected by a channel, and it is judged whether there is a specified target in the video data of the channel. If it exists, the video data of the channel is stored, and the above operations are performed on each channel by polling to ensure The electronic device stores the video data collected by each channel with the specified target.
- the amount of stored video data is less than the amount of data stored in the traditional way, and the storage space required is less than the storage space required by the traditional way, saving the storage of video
- the hardware cost of the data electronic device Provides polling and pre-recording functions, and performs one channel identification and storage operations at a time. Compared with full channels, it requires less computing resources while ensuring full channel identification. And, if for a certain channel, it is recognized that there is no specified target in the recording data of the channel during a certain recognition, stop continuing to store the recording data of the channel to ensure that the electronic device does not store too much non-existent
- the video data of the designated target is stored as far as possible and only the video data of the designated target is stored, which further saves the hardware cost of the electronic equipment.
- the electronic equipment mainly includes several software and/or hardware units: video capture unit, code stream packaging unit, video unit, storage unit, configuration unit and deep learning processing unit.
- video capture unit mainly includes several software and/or hardware units: video capture unit, code stream packaging unit, video unit, storage unit, configuration unit and deep learning processing unit.
- code stream packaging unit mainly includes several software and/or hardware units: video capture unit, code stream packaging unit, video unit, storage unit, configuration unit and deep learning processing unit.
- the connection relationship is shown in Figure 3.
- the video acquisition unit is mainly responsible for the access of video analog signals or digital signals;
- the code stream encapsulation unit is mainly responsible for encapsulating video data into RTP (Reliable Transport Protocol) formats;
- the storage unit is mainly responsible for the storage of video data;
- the configuration unit It is mainly responsible for the configuration and management of the video recording unit;
- the deep learning processing unit is mainly responsible for identifying the input video data, identifying people, cars or other objects of interest to users in the video data.
- the main body that implements the video data storage method provided in the embodiments of the present application is a DVR or NVR.
- the flow of the DVR's implementation of the video data storage method is shown in Figure 4. Since the input of the DVR is analog data, there is no need to decode the video data before target recognition.
- the left side of Figure 4 is the video data purification flow, including the video capture unit. Collect video data and use FRCNN to identify the target. If the specified target is recognized, the storage notification of the corresponding channel will be opened through the configuration unit. Otherwise, the storage stop notification will be initiated through the configuration unit. After a test, it is determined whether to poll the next channel, and if yes, then receive The video data collected by the next channel.
- the right side of Figure 4 shows the video data storage process.
- the corresponding channel collects video data, encodes H264 or H265, and then encodes the video data.
- the video pre-recording unit pre-records the video, and the pre-recording unit receives the video data purification process.
- the video data sent is stored for video data. This completes the video data storage process of one channel.
- the flow of the NVR's implementation of the video data storage method is shown in Figure 5. Since the NVR input is IPC data, the video data needs to be decoded before target recognition, which is different from the DVR processing flow.
- the video data on the left side of Figure 5 In the purification process, video decoding only decodes the I frame of the video, so that the specified target in the video data can be decoded and identified quickly and efficiently, and in the video data storage process on the right side of Figure 5, video encoding is not required.
- an embodiment of the present application provides a video data storage device.
- the device may include:
- the obtaining module 610 is configured to sequentially obtain the video recording data of each channel in a polling manner
- the recognition module 620 is configured to perform target recognition on the recording data of a channel after acquiring the recording data of a channel each time, and identify the designated target in the recording data of the channel;
- the storage module 630 is configured to store the video data of the channel if there is a designated target in the video data of the channel.
- the recording data may include audio data
- the identification module 620 may be specifically used for:
- the recording data includes video data
- the identification module 620 may be specifically used for:
- each image data to be recognized it is determined whether there is a designated target in the video data of the channel.
- the device may also include:
- the stop module is used to stop the storage of the channel's video data if there is no designated target in the channel's video data.
- the storage module 630 can be specifically used for:
- the recording data of each channel is obtained in turn according to the polling method.
- the recording data of a channel is identified by the target, and the recording data of the channel is identified Specify target, if there is a specified target in the recording data of this channel, the recording data of this channel will be stored.
- Target recognition is performed on the video data collected by a channel, and it is judged whether there is a specified target in the video data of the channel. If it exists, the video data of the channel is stored, and the above operations are performed on each channel by polling to ensure
- the electronic device stores the video data collected by each channel with the specified target.
- the amount of stored video data is less than the amount of data stored in the traditional way, and the storage space required is less than the storage space required by the traditional way, saving the storage of video
- the hardware cost of the data electronic device is less than the amount of data stored in the traditional way, and the storage space required is less than the storage space required by the traditional way, saving the storage of video The hardware cost of the data electronic device.
- An embodiment of the present application provides an electronic device, as shown in FIG. 7, including a processor 701 and a memory 702, where the memory 702 stores machine executable instructions that can be executed by the processor 701, and the machine Executable instructions are loaded and executed by the processor 701 to implement the recording data storage method provided in the embodiment of the present application.
- the foregoing memory may include RAM (Random Access Memory, random access memory), and may also include NVM (Non-volatile Memory, non-volatile memory), such as at least one disk storage.
- NVM Non-volatile Memory, non-volatile memory
- the memory may also be at least one storage device located far away from the foregoing processor.
- the above-mentioned processor may be a general-purpose processor, including CPU (Central Processing Unit), NP (Network Processor, network processor), etc.; it may also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array, field programmable gate array) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.
- CPU Central Processing Unit
- NP Network Processor, network processor
- DSP Digital Signal Processor
- ASIC Application Specific Integrated Circuit
- FPGA Field-Programmable Gate Array, field programmable gate array
- other programmable logic devices discrete gates or transistor logic devices, discrete hardware components.
- the memory 702 and the processor 701 may perform data transmission through a wired connection or a wireless connection, and the electronic device and other devices may communicate through a wired communication interface or a wireless communication interface. What is shown in FIG. 7 is only an example of data transmission through the bus, and is not intended to limit the specific connection mode.
- the processor reads the machine executable instructions stored in the memory, and loads and executes the machine executable instructions, so as to achieve: according to the polling method, the video data of each channel can be obtained in turn, and every time After acquiring the video data of a channel, perform target recognition on the video data of the channel, identify the designated target in the video data of the channel, and store the video data of the channel if the designated target exists in the video data of the channel. Target recognition is performed on the video data collected by a channel, and it is judged whether there is a specified target in the video data of the channel.
- the video data of the channel is stored, and the above operations are performed on each channel by polling to ensure
- the electronic device stores the video data collected by each channel with the specified target.
- the amount of stored video data is less than the amount of data stored in the traditional way, and the storage space required is less than the storage space required by the traditional way, saving the storage of video
- the hardware cost of the data electronic device is less than the amount of data stored in the traditional way, and the storage space required is less than the storage space required by the traditional way, saving the storage of video The hardware cost of the data electronic device.
- an embodiment of the present application also provides a machine-readable storage medium that stores machine-executable instructions in the machine-readable storage medium.
- the present application is implemented.
- the recording data storage method provided by the embodiment.
- the machine-readable storage medium stores machine executable instructions that execute the recording data storage method provided by the embodiment of the present application at runtime, so it can be realized: according to the polling method, the recording of each channel is obtained in turn Data, each time the recording data of a channel is obtained, target recognition is performed on the recording data of the channel, and the specified target in the recording data of the channel is identified. If the specified target exists in the recording data of the channel, the channel is stored Video data. Target recognition is performed on the video data collected by a channel, and it is judged whether there is a specified target in the video data of the channel.
- the video data of the channel is stored, and the above operations are performed on each channel by polling to ensure
- the electronic device stores the video data collected by each channel with the specified target.
- the amount of stored video data is less than the amount of data stored in the traditional way, and the storage space required is less than the storage space required by the traditional way, saving the storage of video
- the hardware cost of the data electronic device is less than the amount of data stored in the traditional way, and the storage space required is less than the storage space required by the traditional way, saving the storage of video The hardware cost of the data electronic device.
- the embodiment of the present application also provides an application program for executing at runtime: the recording data storage method provided by the embodiment of the present application.
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Abstract
Description
Claims (13)
- 一种录像数据存储方法,其特征在于,所述方法包括:按照轮询的方式,依次获取各通道的录像数据;在每次获取到一个通道的录像数据后,对所述一个通道的录像数据进行目标识别,识别所述一个通道的录像数据中的指定目标;若所述一个通道的录像数据中存在所述指定目标,则存储所述一个通道的录像数据。
- 根据权利要求1所述的方法,其特征在于,所述录像数据包括音频数据;所述对所述一个通道的录像数据进行目标识别,识别所述一个通道的录像数据中的指定目标,包括:对所述一个通道的音频数据进行预处理,得到待识别音频数据;采用滑动窗口方式,从所述待识别音频数据中,获取不同时域的音频单元;采用第一预设深度学习模型,对各音频单元进行音频识别,得到所述各音频单元的识别结果;采用预先建立的语言模型库,对所述各音频单元的识别结果进行相似度匹配;根据所述各音频单元对应的匹配结果,判断所述一个通道的音频数据中是否存在指定目标。
- 根据权利要求1所述的方法,其特征在于,所述录像数据包括视频数据;所述对所述一个通道的录像数据进行目标识别,识别所述一个通道的录像数据中的指定目标,包括:对所述一个通道的视频数据中的各图像数据分别进行预处理,得到各待识别图像数据;采用第二预设深度学习模型,对所述各待识别图像数据进行目标识别;根据所述各待识别图像数据的识别结果,判断所述一个通道的视频数据中是否存在指定目标。
- 根据权利要求1所述的方法,其特征在于,在所述在每次获取到一个通道的录像数据后,对所述一个通道的录像数据进行目标识别,识别所述一个通道的录像数据中的指定目标之后,所述方法还包括:若所述一个通道的录像数据中不存在所述指定目标,则停止对所述一个通道的录像数据的存储。
- 根据权利要求1所述的方法,其特征在于,所述若所述一个通道的录像数据中存在所述指定目标,则存储所述一个通道的录像数据,包括:若所述一个通道的录像数据中存在所述指定目标,则从所述一个通道对应的缓冲区中读取预设时段内的录像数据,所述一个通道对应的缓存区中存储的是当前时刻之前的预设时段内所述一个通道采集的录像数据;存储所述预设时段内的录像数据及获取到的所述一个通道的录像数据。
- 一种录像数据存储装置,其特征在于,所述装置包括:获取模块,用于按照轮询的方式,依次获取各通道的录像数据;识别模块,用于在每次获取到一个通道的录像数据后,对所述一个通道的录像数据进行目标识别,识别所述一个通道的录像数据中的指定目标;存储模块,用于若所述一个通道的录像数据中存在所述指定目标,则存储所述一个通道的录像数据。
- 根据权利要求6所述的装置,其特征在于,所述录像数据包括音频数据;所述识别模块,具体用于:对所述一个通道的音频数据进行预处理,得到待识别音频数据;采用滑动窗口方式,从所述待识别音频数据中,获取不同时域的音频单元;采用第一预设深度学习模型,对各音频单元进行音频识别,得到所述各音频单元的识别结果;采用预先建立的语言模型库,对所述各音频单元的识别结果进行相似度匹配;根据所述各音频单元对应的匹配结果,判断所述一个通道的音频数据中是否存在指定目标。
- 根据权利要求6所述的装置,其特征在于,所述录像数据包括视频数据;所述识别模块,具体用于:对所述一个通道的视频数据中的各图像数据分别进行预处理,得到各待识别图像数据;采用第二预设深度学习模型,对所述各待识别图像数据进行目标识别;根据所述各待识别图像数据的识别结果,判断所述一个通道的视频数据中是否存在指定目标。
- 根据权利要求6所述的装置,其特征在于,所述装置还包括:停止模块,用于若所述一个通道的录像数据中不存在所述指定目标,则停止对所述一个通道的录像数据的存储。
- 根据权利要求6所述的装置,其特征在于,所述存储模块,具体用于:若所述一个通道的录像数据中存在所述指定目标,则从所述一个通道对应的缓冲区中读取预设时段内的录像数据,所述一个通道对应的缓存区中存储的是当前时刻之前的预设时段内所述一个通道采集的录像数据;存储所述预设时段内的录像数据及获取到的所述一个通道的录像数据。
- 一种电子设备,其特征在于,包括处理器和存储器,其中,所述存储器存储有能够被所述处理器执行的机器可执行指令,所述机器可执行指令由所述处理器加载并执行,以实现权利要求1-5任一项所述的方法。
- 一种机器可读存储介质,其特征在于,所述机器可读存储介质内存 储有机器可执行指令,所述机器可执行指令在被处理器加载并执行时,实现权利要求1-5任一项所述的方法。
- 一种应用程序,其特征在于,用于在运行时执行:权利要求1-5任一项所述的方法。
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Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007124526A (ja) * | 2005-10-31 | 2007-05-17 | Hitachi Ltd | 画像監視記録装置及び画像監視記録システム |
CN101299812A (zh) * | 2008-06-25 | 2008-11-05 | 北京中星微电子有限公司 | 视频分析和存储方法、系统,及视频检索方法、系统 |
CN101867786A (zh) * | 2009-04-20 | 2010-10-20 | 中兴通讯股份有限公司 | 一种视频监控方法及装置 |
CN102665054A (zh) * | 2012-05-10 | 2012-09-12 | 江苏友上科技实业有限公司 | 一种用人脸快速检索的网络视频录像机系统 |
WO2013069565A1 (ja) * | 2011-11-09 | 2013-05-16 | ステラグリーン株式会社 | 撮影記録装置 |
CN103188468A (zh) * | 2011-12-30 | 2013-07-03 | 支录奎 | 有用视频识别录像机的研究 |
CN104202575A (zh) * | 2014-09-17 | 2014-12-10 | 广州中国科学院软件应用技术研究所 | 一种视频监控处理方法及系统 |
CN104836992A (zh) * | 2015-05-08 | 2015-08-12 | 无锡天脉聚源传媒科技有限公司 | 一种监控视频录制方法及装置 |
CN106157952A (zh) * | 2016-08-30 | 2016-11-23 | 北京小米移动软件有限公司 | 声音识别方法及装置 |
CN109344688A (zh) * | 2018-08-07 | 2019-02-15 | 江苏大学 | 一种基于卷积神经网络的监控视频中人的自动识别方法 |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007241377A (ja) * | 2006-03-06 | 2007-09-20 | Sony Corp | 検索システム、撮像装置、データ保存装置、情報処理装置、撮像画像処理方法、情報処理方法、プログラム |
CN101998138A (zh) * | 2009-08-25 | 2011-03-30 | 北京达鸣慧科技有限公司 | 电视频道监控系统及其实时监控方法 |
JP5962916B2 (ja) * | 2012-11-14 | 2016-08-03 | パナソニックIpマネジメント株式会社 | 映像監視システム |
CN104038717B (zh) * | 2014-06-26 | 2017-11-24 | 北京小鱼在家科技有限公司 | 一种智能录制系统 |
CN108198545B (zh) * | 2017-12-19 | 2021-11-02 | 安徽建筑大学 | 一种基于小波变换的语音识别方法 |
-
2019
- 2019-04-29 CN CN201910355683.5A patent/CN111866444A/zh active Pending
-
2020
- 2020-04-08 WO PCT/CN2020/083732 patent/WO2020220951A1/zh active Application Filing
Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007124526A (ja) * | 2005-10-31 | 2007-05-17 | Hitachi Ltd | 画像監視記録装置及び画像監視記録システム |
CN101299812A (zh) * | 2008-06-25 | 2008-11-05 | 北京中星微电子有限公司 | 视频分析和存储方法、系统,及视频检索方法、系统 |
CN101867786A (zh) * | 2009-04-20 | 2010-10-20 | 中兴通讯股份有限公司 | 一种视频监控方法及装置 |
WO2013069565A1 (ja) * | 2011-11-09 | 2013-05-16 | ステラグリーン株式会社 | 撮影記録装置 |
CN103188468A (zh) * | 2011-12-30 | 2013-07-03 | 支录奎 | 有用视频识别录像机的研究 |
CN102665054A (zh) * | 2012-05-10 | 2012-09-12 | 江苏友上科技实业有限公司 | 一种用人脸快速检索的网络视频录像机系统 |
CN104202575A (zh) * | 2014-09-17 | 2014-12-10 | 广州中国科学院软件应用技术研究所 | 一种视频监控处理方法及系统 |
CN104836992A (zh) * | 2015-05-08 | 2015-08-12 | 无锡天脉聚源传媒科技有限公司 | 一种监控视频录制方法及装置 |
CN106157952A (zh) * | 2016-08-30 | 2016-11-23 | 北京小米移动软件有限公司 | 声音识别方法及装置 |
CN109344688A (zh) * | 2018-08-07 | 2019-02-15 | 江苏大学 | 一种基于卷积神经网络的监控视频中人的自动识别方法 |
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