CN109905666B - Image processing method - Google Patents
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- CN109905666B CN109905666B CN201910115954.XA CN201910115954A CN109905666B CN 109905666 B CN109905666 B CN 109905666B CN 201910115954 A CN201910115954 A CN 201910115954A CN 109905666 B CN109905666 B CN 109905666B
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Abstract
The invention provides an image processing method and an intelligent terminal, wherein the method comprises the following steps: the remote processing terminal receives video image information transmitted by the wireless sensor network; the remote processing terminal correspondingly decompresses the video image information and stores each decompressed video image information in a partition mode according to the identification of the sensor node; the remote processing terminal receives an information sending request sent by the intelligent terminal, wherein the information sending request comprises a sensor node identifier for requesting to send video image information; and the remote processing terminal sends the video image information corresponding to the sensor node identification which requests to send the video image information to the intelligent terminal according to the information sending request.
Description
Technical Field
The invention relates to the technical field of video image acquisition and processing, in particular to an image processing method and an intelligent terminal.
Background
The wiring video monitoring system in the related art mainly comprises a network video server, a database server, a camera connected with the server through a network and the like. The system is generally large in size, complex in network topology, high in cost, and difficult to deploy in some harsh or special application environments. Meanwhile, the traditional video monitoring system mainly provides a video image information acquisition function, and cannot provide real-time image classification and other functions.
Disclosure of Invention
In order to solve the problems, the invention provides an image processing method and an intelligent terminal.
The purpose of the invention is realized by adopting the following technical scheme:
a first aspect of the present invention provides an image processing method, including:
the remote processing terminal receives video image information transmitted by the wireless sensor network, and the video image information is collected by the video monitoring device and compressed by an image compression algorithm to be suitable for transmission of the wireless sensor network; the wireless sensor network comprises a plurality of sensor nodes and a sink node connected with the remote processing terminal, and each sensor node is at least connected with a video monitoring device to collect correspondingly compressed video image information;
the remote processing terminal correspondingly decompresses the video image information and stores each decompressed video image information in a partition mode according to the identification of the sensor node;
the remote processing terminal receives an information sending request sent by the intelligent terminal, wherein the information sending request comprises a sensor node identifier for requesting to send video image information;
and the remote processing terminal sends the video image information corresponding to the sensor node identification which requests to send the video image information to the intelligent terminal according to the information sending request.
The invention is based on the wireless sensor network technology, overcomes the defects of high cost, difficult system deployment and larger installation and maintenance difficulty of the traditional wiring video monitoring system and a network camera, realizes the integration of acquisition and processing of video image information through the access integration of a remote processing terminal and a wireless sensor network, can realize the classification processing of the image information, and is convenient for a user to quickly and conveniently acquire the required video image information through an intelligent terminal.
According to one enabling aspect of the first aspect of the invention, the method further comprises:
the remote processing terminal extracts image features of video images of the same sensor node after decompression, and compares the similarity of the two image features to obtain a similarity value of the two image features;
and if the similarity value is lower than a preset similarity threshold value, the remote processing terminal randomly selects one of the sequential video images for deletion.
The embodiment realizes the screening of the video image information collected by the same sensor node through the similarity comparison method, is beneficial to saving the storage space of the remote processing terminal, reduces the storage power consumption of the remote processing terminal, and further provides concise video image information for the intelligent terminal.
According to a manner that can be realized in the first aspect of the present invention, the remote processing terminal includes a database server, a data analysis server and a communication server, the database server is mainly responsible for decompressing the video image information received from the sink node and storing the decompressed video image information into a corresponding database in a partitioned manner, and the data analysis server is mainly responsible for performing similarity comparison analysis and screening processing on the decompressed video image; the communication server provides corresponding access interfaces for the sink node and the intelligent terminal, and provides functions of inquiry, deletion, marking, importing and exporting for the intelligent terminal by calling the stored video image information.
In one enabling manner of the first aspect of the present invention, the method further includes:
the remote processing terminal receives an encryption instruction of the predetermined intelligent terminal, wherein the encryption instruction comprises a sensor node identifier;
and the remote processing terminal encrypts the video image information corresponding to the sensor node identification in the encryption instruction by adopting a preset encryption algorithm.
According to the embodiment, the video image information appointed by the user is encrypted, so that the important video image information is prevented from being leaked, the privacy of the user is protected, and the safety of the video image information is greatly improved.
A second aspect of the present invention provides an intelligent terminal, which is configured to execute an image processing method as described above.
In the image processing method and the intelligent terminal, each sensor node sends the collected video image information to the sink node according to the communication level of the sensor node, and the method comprises the following steps:
the first-level sensor node adopts a direct communication mode, the second-level sensor node selects a direct communication mode or an indirect communication mode according to the current residual energy of the second-level sensor node, and the third-level sensor node adopts an indirect communication mode;
wherein the direct communication mode is: the sensor node directly sends the acquired video image information to the sink node; the indirect communication mode is as follows: the sensor nodes select one sensor node from the sensor nodes in the communication range of the sensor nodes as a next hop node, and the acquired video image information is sent to the next hop node so as to be forwarded by the next hop node until the video image information is transmitted to the sink node.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a flowchart illustrating an image processing method according to an exemplary embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, an embodiment of the first aspect of the present invention provides an image processing method, including:
s1, the remote processing terminal receives video image information transmitted by the wireless sensor network, the video image information is collected by the video monitoring device and compressed by the image compression algorithm to adapt to the transmission of the wireless sensor network; the wireless sensor network comprises a plurality of sensor nodes and a sink node connected with the remote processing terminal, and each sensor node is at least connected with a video monitoring device to collect correspondingly compressed video image information.
S2, the remote processing terminal correspondingly decompresses the video image information and stores each decompressed video image information according to the identification partition of the sensor node.
S3, the remote processing terminal receives an information sending request sent by the intelligent terminal, wherein the information sending request comprises the sensor node identification which requests to send the video image information.
S4, the remote processing terminal sends the video image information corresponding to the sensor node identification of the request for sending the video image information to the intelligent terminal according to the information sending request.
The invention is based on the wireless sensor network technology, overcomes the defects of high cost, difficult system deployment and larger installation and maintenance difficulty of the traditional wiring video monitoring system and a network camera, realizes the integration of acquisition and processing of video image information through the access integration of a remote processing terminal and a wireless sensor network, can realize the classification processing of the image information, and is convenient for a user to quickly and conveniently acquire the required video image information through an intelligent terminal.
According to one enabling aspect of the first aspect of the invention, the method further comprises:
the remote processing terminal extracts image features of video images of the same sensor node after decompression, and compares the similarity of the two image features to obtain a similarity value of the two image features;
and if the similarity value is lower than a preset similarity threshold value, the remote processing terminal randomly selects one of the sequential video images for deletion.
The embodiment realizes the screening of the video image information collected by the same sensor node through the similarity comparison method, is beneficial to saving the storage space of the remote processing terminal, reduces the storage power consumption of the remote processing terminal, and further provides concise video image information for the intelligent terminal.
According to a manner that can be realized in the first aspect of the present invention, the remote processing terminal includes a database server, a data analysis server and a communication server, the database server is mainly responsible for decompressing the video image information received from the sink node and storing the decompressed video image information into a corresponding database in a partitioned manner, and the data analysis server is mainly responsible for performing similarity comparison analysis and screening processing on the decompressed video image; the communication server provides corresponding access interfaces for the sink node and the intelligent terminal, and provides functions of inquiry, deletion, marking, importing and exporting for the intelligent terminal by calling the stored video image information.
In one enabling manner of the first aspect of the present invention, the method further includes:
the remote processing terminal receives an encryption instruction of the predetermined intelligent terminal, wherein the encryption instruction comprises a sensor node identifier;
and the remote processing terminal encrypts the video image information corresponding to the sensor node identification in the encryption instruction by adopting a preset encryption algorithm.
According to the embodiment, the video image information appointed by the user is encrypted, so that the important video image information is prevented from being leaked, the privacy of the user is protected, and the safety of the video image information is greatly improved.
The embodiment of the second aspect of the present invention provides an intelligent terminal, where the intelligent terminal is configured to execute an image processing method described above.
In the image processing method and the intelligent terminal, each sensor node sends the collected video image information to the sink node according to the communication level of the sensor node, and the method comprises the following steps:
the first-level sensor node adopts a direct communication mode, the second-level sensor node selects a direct communication mode or an indirect communication mode according to the current residual energy of the second-level sensor node, and the third-level sensor node adopts an indirect communication mode;
wherein the direct communication mode is: the sensor node directly sends the acquired video image information to the sink node; the indirect communication mode is as follows: the sensor nodes select one sensor node from the sensor nodes in the communication range of the sensor nodes as a next hop node, and the acquired video image information is sent to the next hop node so as to be forwarded by the next hop node until the video image information is transmitted to the sink node;
wherein, the adjustable communication distance range of each sensor node in the network is [ Z ]min,Zmax]The communication level of the sensor node is determined by the sink node, and specifically comprises the following steps:
(1) when a network is initialized, the sink node broadcasts hello messages to all sensor nodes and starts a timer, after all sensor nodes receive the hello messages, the sensor nodes calculate own communication weight and send feedback messages to the sink node, wherein the feedback messages comprise sensor node identifiers, the communication weight and position information:
in the formula, GyCommunication weight of $ for sensor node, NyFor the number of sensor nodes located within $ communication range of the sensor node,
is located within the communication range of the sensor node and has a distance less than $
Number of sensor nodes of HyxDistance of sensor node $ from its x-th sensor node in communication range, a1、a2Is a preset weight coefficient, a1+a2＝1；
(2) Presetting a first direct communication distance threshold Hτ1Second direct communication distance threshold Hτ2，Zmax>Hτ2>Hτ1The sink node distributes the communication level of the sensor node according to the position information and the communication weight of each sensor node, and broadcasts distribution information to each sensor node: if the distance from the sensor node to the sink node is not more than Hτ1Or the distance from the sensor node to the sink node is [ H ]τ1,Hτ2]If the communication weight is greater than 1/3, the communication level of the sensor node is assigned as one level; if the distance from the sensor node to the sink node is [ H ]τ1,Hτ2]If the communication weight is not more than 1/3, the communication level of the sensor node is allocated to be two levels; if the distance from the sensor node to the sink node is greater than Hτ2The communication level of the sensor node is assigned to three levels.
In this embodiment, each sensor node sends the collected video image information to the sink node according to its own communication level, where the communication level is determined by the sink node according to the communication weight and the location information of the sensor node. The embodiment creatively provides a new index of the communication weight, and it can be seen that the denser the neighbor nodes of the sensor node are, the more the neighbor nodes are close to the sensor node, the larger the communication weight of the sensor node is. The communication weight is calculated by each sensor node and fed back to the sink node, so that the calculation load of each sensor node is balanced, and the efficiency of distributing the communication level to each sensor node is improved; by setting the communication level, the flexibility of sensor node routing is improved, sensor nodes with dense peripheral neighbor nodes can preferentially and directly communicate with the sink node, unnecessary data forwarding is avoided, and energy consumption in the aspect of sending video image information is saved due to the fact that sensor nodes far away from the sink node adopt an indirect communication mode.
In one embodiment, the secondary sensor node selects a direct communication mode or an indirect communication mode according to its current remaining energy, specifically: the distance between the secondary sensor node u and the nearest sensor node is set as HminuThe distance from the node of the second-level sensor to the sink node is HouIf H isminu-HouThe secondary sensor node u is more than or equal to 0, and a direct communication mode is always selected by the secondary sensor node u; if H isminu-Hou<0, the secondary sensor node u calculates the communication distance threshold value Z of the secondary sensor node uTuIf Z isTu≥HouIf yes, the secondary sensor node u selects a direct communication mode; if Z isTu<HouIf so, the secondary sensor node u selects an indirect communication mode, and takes the sensor node closest to the secondary sensor node u as a next hop node; wherein the communication distance threshold value ZTuCalculated according to the following formula:
in the formula, Q0uIs the initial energy, Q, of the secondary sensor node uuIs the current remaining energy of the secondary sensor node u.
In this embodiment, the secondary sensor node can adjust its own communication mode according to its own current residual energy, which improves the flexibility of routing of the secondary sensor node. The embodiment of the invention innovatively designs the measurement index of the communication distance threshold according to the energy factor, and determines the communication mode of the secondary sensor node according to the comparison result of the communication distance threshold and the distance to the sink node, so that the method is beneficial to optimally saving the energy of the secondary sensor node and delaying the energy consumption of the secondary sensor node on the premise of ensuring the reliability of the secondary sensor node in the aspect of sending video image information, thereby prolonging the working period of the secondary sensor node and further prolonging the service life of the wireless sensor network as a whole.
In one embodiment, when the third-level sensor node selects the next-hop node, the following steps are specifically performed:
(1) the three-level sensor nodes acquire sensor nodes which are closer to the sink node relative to the sensor nodes in the communication range of the three-level sensor nodes as alternative nodes, and an alternative node set is constructed;
(2) initially, the three-level sensor node determines the selection distance as ZmaxAnd selecting the closest Z to the candidate node setmaxThe alternative node of (2) is used as a next hop node;
(3) every other preset period delta T, the selection distance is updated by the third-level sensor node according to the following formula, and the alternative node with the distance closest to the current updated selection distance is selected as the next-hop node again:
in the formula, Zb(σ + Δ T) is the selected distance, Z, of the currently updated tertiary sensor node bb(σ) is the selected distance, Q, of the last updated tertiary sensor node bbBeing three-level sensor node bCurrent residual energy, Q0bIs the initial energy of the three-level sensor node b, delta is a preset energy-based influence factor, and the value range of delta is [2.5 pi, 3 pi]。
When the number of updating reaches a preset number threshold, or the updated selection distance is less than ZminAnd when the node is updated, the node of the third-level sensor stops updating the node of the next hop.
The embodiment provides a specific mechanism for selecting the next-hop node by the three-level sensor node, wherein a selection index for selecting the distance is provided. According to the embodiment, the selection distance is determined according to the energy of the three-level sensor node, and the alternative node which is closest to the current updated selection distance is selected as the next hop node, so that the number of the next hop nodes for forwarding the video image information is reduced as much as possible on the premise of ensuring the reliable forwarding of the video image information, and the efficiency of forwarding the video image information is improved.
It will be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the system is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the system and the terminal described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
From the above description of embodiments, it is clear for a person skilled in the art that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For a hardware implementation, a processor may be implemented in one or more of the following units: an application specific integrated circuit, a digital signal processor, a digital signal processing system, a programmable logic device, a field programmable gate array, a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the procedures of an embodiment may be performed by a computer program instructing associated hardware. In practice, the program may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. The computer-readable medium can include, but is not limited to, random access memory, read only memory images, electrically erasable programmable read only memory or other optical disk storage, magnetic disk storage media or other magnetic storage systems, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (4)
1. An image processing method, characterized by comprising:
the remote processing terminal receives video image information transmitted by the wireless sensor network, and the video image information is collected by the video monitoring device and compressed by an image compression algorithm to be suitable for transmission of the wireless sensor network; the wireless sensor network comprises a plurality of sensor nodes and a sink node connected with the remote processing terminal, and each sensor node is at least connected with a video monitoring device to collect correspondingly compressed video image information;
the remote processing terminal correspondingly decompresses the video image information and stores each decompressed video image information in a partition mode according to the identification of the sensor node;
the remote processing terminal receives an information sending request sent by the intelligent terminal, wherein the information sending request comprises a sensor node identifier for requesting to send video image information;
the remote processing terminal sends video image information corresponding to the sensor node identification which requests to send the video image information to the intelligent terminal according to the information sending request; each sensor node sends the collected video image information to the sink node according to the communication level of the sensor node, and the method comprises the following steps:
the first-level sensor node adopts a direct communication mode, the second-level sensor node selects a direct communication mode or an indirect communication mode according to the current residual energy of the second-level sensor node, and the third-level sensor node adopts an indirect communication mode;
wherein the direct communication mode is: the sensor node directly sends the acquired video image information to the sink node; the indirect communication mode is as follows: the sensor nodes select one sensor node from the sensor nodes in the communication range of the sensor nodes as a next hop node, and the acquired video image information is sent to the next hop node so as to be forwarded by the next hop node until the video image information is transmitted to the sink node;
wherein, the adjustable communication distance range of each sensor node in the network is [ Z ]min,Zmax]The communication level of the sensor node is determined by the sink node, and specifically comprises the following steps:
(1) when a network is initialized, the sink node broadcasts hello messages to all sensor nodes and starts a timer, after all sensor nodes receive the hello messages, the sensor nodes calculate own communication weight and send feedback messages to the sink node, wherein the feedback messages comprise sensor node identifiers, the communication weight and position information:
in the formula, GyIs the communication weight of the sensor node y, NyFor the number of sensor nodes located within the communication range of sensor node y,
the distance between the sensor node y and the sensor node y within the communication range of the sensor node y is less than
Number of sensor nodes of HyxIs the distance, a, of the sensor node y from the x-th sensor node within its communication range1、a2Is a preset weight coefficient, a1+a2＝1；
(2) Presetting a first direct communication distance threshold Hτ1Second direct communication distance threshold Hτ2，Zmax>Hτ2>Hτ1The sink node distributes the communication level of the sensor node according to the position information and the communication weight of each sensor node, and broadcasts distribution information to each sensor node: if the distance from the sensor node to the sink node is not more than Hτ1Or the distance from the sensor node to the sink node is [ H ]τ1,Hτ2]If the communication weight is greater than 1/3, the communication level of the sensor node is assigned as one level; if the distance from the sensor node to the sink node is [ H ]τ1,Hτ2]If the communication weight is not more than 1/3, the communication level of the sensor node is allocated to be two levels; if the distance from the sensor node to the sink node is greater than Hτ2The communication level of the sensor node is assigned to three levels.
2. An image processing method according to claim 1, characterized in that the method further comprises:
the remote processing terminal extracts image features of video images of the same sensor node after decompression, and compares the similarity of the two image features to obtain a similarity value of the two image features;
and if the similarity value is lower than a preset similarity threshold value, the remote processing terminal randomly selects one of the sequential video images for deletion.
3. An image processing method according to claim 1, characterized in that the method further comprises:
the remote processing terminal receives a predetermined encryption instruction of the intelligent terminal, wherein the encryption instruction comprises a sensor node identifier;
and the remote processing terminal encrypts the video image information corresponding to the sensor node identification in the encryption instruction by adopting a preset encryption algorithm.
4. An image processing method according to any one of claims 1 to 3, wherein the remote processing terminal comprises a database server, a data analysis server and a communication server, the database server is mainly responsible for decompressing the video image information received from the sink node and storing the decompressed video image information into the corresponding database in a partitioned manner, and the data analysis server is mainly responsible for performing similarity comparison analysis and screening processing on the decompressed video image; the communication server provides corresponding access interfaces for the sink node and the intelligent terminal, and provides functions of inquiry, deletion, marking, importing and exporting for the intelligent terminal by calling the stored video image information.
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