CN110650316A - Intelligent patrol and early warning processing method and device, electronic equipment and storage medium - Google Patents
Intelligent patrol and early warning processing method and device, electronic equipment and storage medium Download PDFInfo
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- 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
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
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Abstract
The application provides an intelligent patrol and early warning processing method, an intelligent patrol and early warning processing device, electronic equipment and a storage medium, wherein the method comprises the following steps: when the patrol time is up, acquiring video monitoring images of a plurality of patrol routes to execute a preset patrol plan; judging whether an abnormal condition exists in the monitoring area of any one of the patrol routes according to the video monitoring image; if the abnormal condition exists, outputting an early warning prompt, and acquiring the distance between the position information of the monitoring area where the abnormal condition occurs and the position information of each first target object; and dispatching an exception handling task to the first target object with the distance smaller than a specified threshold value. The embodiment of the application is beneficial to improving the efficiency of patrol and abnormal condition processing while reducing the labor cost overhead.
Description
Technical Field
The application relates to the technical field of intelligent patrol, in particular to an intelligent patrol and early warning processing method and device, electronic equipment and a storage medium.
Background
Along with the application of the internet of things technology in park construction, the progress of smart park construction in China is greatly accelerated, the smart park construction is an important way for realizing intelligent and modernized park management, various resources can be optimally configured and integrated and utilized by constructing the smart park, and the economic, industrial and ecological structures of the whole park are improved. At present, the security defense system of most parks is responsible for property management, the property management adopts a manual patrol mode, fixed patrol points are arranged in the parks, patrol personnel regularly and regularly go to the field for patrol according to a patrol plan arranged inside and use a patrol bar for induction as a patrol certificate. The manual patrol mode ensures that a certain number of patrol personnel need to be arranged on each route in the garden, and the patrol efficiency is relatively low while huge labor cost is brought.
Disclosure of Invention
In view of the above technical problems, the present application provides an intelligent patrol and early warning processing method, apparatus, electronic device and storage medium, which are beneficial to improving patrol and abnormal condition processing efficiency while reducing human cost overhead.
In order to achieve the above object, a first aspect of the embodiments of the present application provides an intelligent patrol and early warning processing method, including:
when the patrol time is up, acquiring video monitoring images of a plurality of patrol routes to execute a preset patrol plan;
judging whether an abnormal condition exists in the monitoring area of any one of the patrol routes according to the video monitoring image;
if the abnormal condition exists, performing abnormal condition alarm operation, and acquiring the distance between the position information of the monitoring area where the abnormal condition occurs and the position information of each first target object;
and dispatching an exception handling task to the first target object with the distance smaller than a specified threshold value.
With reference to the first aspect, in a possible implementation manner, each of the patrol routes is provided with a plurality of monitoring devices, and each of the monitoring devices is responsible for monitoring a designated monitoring area; the acquiring of the video monitoring images of the plurality of patrol routes to execute the preset patrol plan includes:
reading the preset patrol plan, wherein the preset patrol plan comprises patrol sequences of the patrol routes;
and acquiring a video monitoring image of each monitoring area of each patrol route in the plurality of patrol routes according to the patrol sequence of the plurality of patrol routes so as to execute a preset patrol plan.
With reference to the first aspect, in a possible implementation manner, the determining, according to the video surveillance image, whether an abnormal condition exists in a surveillance area of any one of the patrol routes includes:
performing key point detection on the video monitoring image of each monitoring area and a prestored comparison image by adopting a scale invariant feature conversion algorithm, and acquiring a corresponding vector expression, wherein the comparison image is an image of each monitoring area without abnormal conditions;
aiming at each key point of the video monitoring image of each monitoring area, selecting two key points with highest orientation quantity expression similarity from the comparison images to form a candidate key point matching pair;
screening the candidate key point matching pairs to obtain a preset number of target key point matching pairs, and carrying out affine transformation on the video monitoring image and the comparison image of each monitoring area based on the preset number of target key point matching pairs;
and performing edge detection and color detection on the video monitoring images of each monitoring area subjected to affine transformation and the comparison image to obtain an edge detection result and a color detection result, wherein if any one of the video monitoring images of each monitoring area has edge detection abnormality or color detection abnormality, the video monitoring images are considered to have abnormal conditions, otherwise, the video monitoring images are not considered to have abnormal conditions.
With reference to the first aspect, in a possible implementation manner, the performing edge detection on the video surveillance image of each surveillance area subjected to affine transformation and the comparison image to obtain an edge detection result includes:
dividing the video monitoring image and the comparison image of each monitoring area into M pixel image blocks;
performing edge detection in N directions for each image block in the image blocks of M pixels by M pixels, and calculating an edge distribution histogram of each image block to obtain an N-dimensional histogram vector;
calculating the histogram vector distance between each image block of the video monitoring image of each monitoring area and the corresponding image block in the comparison image;
if any video monitoring image in the video monitoring images of each monitoring area has an image block of which the distance is greater than a first threshold value, determining that the edge detection of the video monitoring image is abnormal;
the color detection is performed on the video monitoring image of each monitoring area subjected to affine transformation and the comparison image to obtain a color detection result, and the color detection result comprises the following steps:
calculating a color mean value of each image block in the M-by-M pixel image blocks in an RGB color space;
calculating the color distance between each image block of the video monitoring image of each monitoring area and the corresponding image block in the comparison image;
and if any video monitoring image in the video monitoring images of each monitoring area has an image block of which the color distance is greater than a second threshold value, determining that the color detection of the video monitoring image is abnormal.
With reference to the first aspect, in one possible implementation, the method further includes:
and in the process of executing the patrol plan, randomly popping up patrol check-in windows on video monitoring image display interfaces corresponding to a plurality of monitoring devices of the current patrol route, so that a second target object is checked in on-line and patrol records are generated.
With reference to the first aspect, in one possible implementation manner, the performing an abnormal situation warning operation includes:
intercepting a current image frame of a video monitoring image of a monitoring area with an abnormal condition and a video monitoring image with preset duration;
and reporting the current image frame, the video monitoring image with the preset duration, the current time information and the position information of the monitoring area with the abnormal condition to a preset alarm center for alarming.
With reference to the first aspect, in one possible implementation, the method further includes:
and receiving the position information reported by the terminal of the first target object when the terminal arrives at the preset time interval.
A second aspect of the embodiments of the present application provides an intelligent patrol and early warning processing apparatus, including:
the acquisition module is used for acquiring video monitoring images of a plurality of patrol routes to execute a preset patrol plan when the patrol time arrives;
the judging module is used for judging whether an abnormal condition exists in a monitoring area of any patrol route in the plurality of patrol routes according to the video monitoring image;
the processing module is used for carrying out abnormal condition alarming operation if abnormal conditions exist, and acquiring the distance between the position information of the monitoring area where the abnormal conditions occur and the position information of each first target object;
the processing module is further used for dispatching an exception handling task to the first target object with the distance smaller than the specified threshold value.
A third aspect of embodiments of the present application provides an electronic device, including: the intelligent patrol and early warning system comprises a processor, a memory and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the steps in the intelligent patrol and early warning processing method.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method implements the steps in the intelligent patrol and early-warning processing method described above.
The above scheme of the present application includes at least the following beneficial effects: when the patrol time arrives, acquiring video monitoring images of a plurality of patrol routes to execute a preset patrol plan; judging whether an abnormal condition exists in the monitoring area of any one of the patrol routes according to the video monitoring image; if the abnormal condition exists, performing abnormal condition alarm operation, and acquiring the distance between the position information of the monitoring area where the abnormal condition occurs and the position information of each first target object; and dispatching an exception handling task to the first target object with the distance smaller than the specified threshold value, thereby realizing intelligent cloud patrol in the security monitoring center, reducing huge cost overhead caused by requiring numerous human power to participate in patrol and early warning handling, and being beneficial to improving the efficiency of patrol and exception handling in the garden.
Drawings
In order to more clearly illustrate the embodiments of the present application 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, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an intelligent patrol and early warning processing system according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of an intelligent patrol and early warning processing method according to an embodiment of the present disclosure;
fig. 3 is an exemplary diagram of a display interface of a video surveillance image according to an embodiment of the present application;
fig. 4 is an exemplary diagram of a patrol check-in provided in an embodiment of the present application;
fig. 5 is a schematic flowchart illustrating a process of determining whether an abnormal condition exists in a corresponding monitored area according to a video surveillance image according to an embodiment of the present application;
fig. 6 is an exemplary diagram of a corresponding image block according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an intelligent patrol and early warning processing apparatus according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. 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 application.
The terms "comprising" and "having," and any variations thereof, as appearing in the specification, claims and drawings of this application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. Furthermore, the terms "first," "second," and "third," etc. are used to distinguish between different objects and are not used to describe a particular order.
First, a network system architecture to which the solution of the embodiments of the present application may be applied will be described by way of example with reference to the accompanying drawings. Referring to fig. 1, fig. 1 is a schematic structural diagram of an intelligent patrol and early-warning processing system provided in an embodiment of the present application, and as shown in fig. 1, the intelligent patrol and early-warning processing system is composed of a campus space and a campus security monitoring center, and each component is in communication through a wired or wireless network. A plurality of monitoring devices are arranged in the garden space, and each monitoring device has a corresponding monitoring area, for example: a monitoring device at the gate machine channel of the garden, a monitoring device of the park parking lot, etc., a park security monitoring center IOC (intelligent Operation center) is accessed to a VCN (Video Cloud Node) Video management service, which can easily obtain the Video monitoring image of the whole park space, when the set patrol time is up, the server executes patrol tasks according to a preset patrol plan to obtain video monitoring images of each monitoring area on a patrol route in the patrol plan, the video monitoring image display interface of the security monitoring center of the garden provides a sign-in function while patrolling, patrol workers can sign in on line on the display interface with sign-in prompt, the server records the identity identification, sign-in time, patrol area and other information of the sign-in personnel, and patrol records are generated and stored in a database for later retrieval or inspection. When an abnormal condition occurs in any monitoring area of the park, such as road surface collapse, fire, broken railing, wall surface falling and the like, the abnormal condition is reported to an alarm center through a preset alarm function, and the reporting at least carries the position information of the monitoring area in which the abnormal condition occurs, such as: and when the monitoring area TCC-A which is responsible for the monitoring equipment with the number of TCC001 has abnormal conditions, the server at least reports the TCC-A to the alarm center. In addition, security personnel on the campus are equipped with specific terminals, such as: the intelligent security service is general, security personnel can set in the terminal to allow the terminal to acquire real-time position information, and then the terminal reports the position information of the security personnel to the security monitoring center of the garden according to a preset position information reporting plan, for example: the location information reporting plan may be reported in real time or according to a preset interval time. When the alarm center receives an abnormal condition alarm, security personnel closest to an abnormal condition monitoring area can be obtained through calculation, an abnormal processing task is dispatched to the security personnel through a terminal of the security personnel, the security personnel go to the monitoring area with the abnormal condition to perform abnormal processing after receiving the abnormal processing task, and after the processing is completed, the security personnel can perform corresponding operation on the terminal to finish the abnormal processing task, for example: uploading a field processing video, filling a processing method and the like, and the whole system forms a closed-loop logic of 'automatic patrol-abnormal condition discovery-alarm-abnormal processing task dispatch-notification-security personnel field processing-abnormal processing ending'. Specifically, the execution subject of the embodiment of the application is a server of a campus security monitoring center, and the server can be a local server, a cloud server, or a server cluster, and is not limited herein.
Based on the above description, please refer to fig. 2, and fig. 2 is a schematic flow chart of an intelligent patrol and early-warning processing method according to an embodiment of the present application, as shown in fig. 2, the intelligent patrol and early-warning processing method includes the following steps: steps S21-S24:
and S21, when the patrol time arrives, acquiring the video monitoring images of a plurality of patrol routes to execute a preset patrol plan.
In the embodiment of the application, the patrol time refers to the execution time of the preset patrol plan, and is usually set to be 24 hours for guaranteeing security of a garden, namely, the patrol plan is executed all day long, and certainly, the patrol time can be set to be patrolled once every other preset time for reducing the loss of hardware such as a server. The patrol plan is a task for patrolling each area of the park, and a plurality of patrol routes form one patrol plan, for example: the three patrol routes of the east gate-west gate, the south gate-west gate and the south gate-north gate of the garden form a patrol plan. And the video monitoring image of each patrol route in the patrol plan is displayed on a display interface of the security monitoring center of the garden, and the server executes the patrol plan by acquiring the video monitoring images of the plurality of patrol routes.
Specifically, each patrol route in the plurality of patrol routes is provided with a plurality of monitoring devices, and each monitoring device in the plurality of monitoring devices is responsible for monitoring a specified monitoring area; the acquiring of the video monitoring images of the plurality of patrol routes to execute the preset patrol plan includes:
reading the preset patrol plan, wherein the preset patrol plan comprises patrol sequences of the patrol routes;
and acquiring a video monitoring image of each monitoring area of each patrol route in the plurality of patrol routes according to the patrol sequence of the plurality of patrol routes so as to execute a preset patrol plan.
As shown in fig. 3, the preset patrol plan includes four patrol routes, which are: the method comprises the following steps that a patrol route A, a patrol route B, a patrol route C and a patrol route D are installed on each patrol route, each monitoring device is responsible for collecting video monitoring images of corresponding monitoring areas in real time, a display interface of a security monitoring center in a garden is provided, the first line represents the video monitoring images of the four monitoring areas of the patrol route A, the second line represents the video monitoring images of the four monitoring areas of the patrol route B, the third line represents the video monitoring images of the four monitoring areas of the patrol route C, the fourth line represents the video monitoring images of the four monitoring areas of the patrol route D, and if a patrol sequence set for a server by a user is that: the patrol route A, the patrol route B, the patrol route C and the patrol route D are sequentially obtained by the server, video monitoring images of four monitoring areas of the patrol route A are firstly obtained by the server, the patrol route A is patrolled, and after the patrol of the four monitoring areas of the patrol route A is finished, four video monitoring images of the patrol route B are automatically obtained for patrol until the patrol of the patrol route D is finished. For some monitoring areas with higher security level, for example: the garden machine electricity room can set corresponding longer patrol time, and the server can acquire video monitoring images longer than other monitoring areas. It should be noted that, during the execution of the patrol plan, sign-in prompts appear at random on the display interfaces of the video monitoring images, as shown in fig. 4, a sign-in window pops up in the 1 st monitoring area of the patrol route a, and the patrol staff completes the patrol sign-in after inputting corresponding information and submitting, of course, fig. 4 is only an example, and does not cause any limitation to the embodiment of the present application.
And S22, judging whether abnormal conditions exist in the monitoring area of any patrol route in the plurality of patrol routes according to the video monitoring image.
In the specific embodiment of the application, the abnormal condition can be various conditions such as suspect, fire, road surface collapse and the like, the abnormal condition can be determined whether to appear in the monitoring area of any one of the patrol routes in a mode of combining manual judgment and machine judgment, and if the abnormal condition is found by patrol workers, the one-key alarm function can be triggered to send out early warning to the alarm center. The server can identify and analyze the acquired video monitoring image, and determine whether an abnormal condition occurs in a monitoring area of any one of the patrol routes by adopting a relevant image processing algorithm, for example: inputting the video monitoring image of the 1 st monitoring area of the patrol route A into a pre-trained convolutional neural network, performing convolution, pooling and other processing, and finally outputting the probability of whether the video monitoring image of the 1 st monitoring area has an abnormal condition, or determining whether the video monitoring image of the 1 st monitoring area comprises the abnormal condition by adopting a scale-invariant feature transformation algorithm in combination with edge detection and color detection, or inputting the video monitoring image of the 1 st monitoring area into a target detection model by adopting a target detection algorithm to detect whether the video monitoring image comprises the abnormal target, outputting a boundary box of the abnormal target if a preset abnormal target can be detected, determining that the 1 st monitoring area has the abnormal condition, and automatically alarming to an alarm center by a server.
In one possible implementation, as shown in fig. 5, the determining whether there is an abnormal situation in the monitored area of any one of the patrol routes according to the video monitoring image includes steps S2201-S2204:
s2201, performing key point detection on the video monitoring image of each monitoring area and a prestored comparison image by adopting a scale-invariant feature conversion algorithm, and acquiring a corresponding vector expression, wherein the comparison image is an image of each monitoring area without abnormal conditions;
in this embodiment of the present application, when performing the key point detection on the video surveillance image in each surveillance area, the key point detection is performed on each frame image of the obtained video surveillance image, where the key point refers to a feature point, and the comparison image is used to compare with the video surveillance image obtained in the patrol process to determine whether there is an image corresponding to the surveillance area with an abnormal condition, for example: and acquiring an image of the 1 st monitoring area in the patrol route A under a normal condition as a comparison image of the video monitoring image of the 1 st monitoring area in the patrol route A, wherein a plurality of comparison images may exist in the same monitoring area. The method comprises the steps of constructing a scale space, then carrying out extreme value detection, identifying potential interest points which are invariable in scale and rotation through a Gaussian differential function, namely key points, positioning the key points, distributing directions to the key points, and finally generating key point descriptors to obtain vector expression of the key points.
S2202, aiming at each key point of the video monitoring image of each monitoring area, selecting two key points with highest orientation quantity expression similarity from the comparison images to form a candidate key point matching pair;
in the specific embodiment of the present application, a brute force matching algorithm is adopted to obtain K candidate keypoints closest to the vector expression of each keypoint g from the comparison image, and the first two candidate keypoints K1 and K2 that are most similar among the K candidate keypoints are selected to form a candidate keypoint matching pair.
S2203, screening the candidate key point matching pairs to obtain a preset number of target key point matching pairs, and carrying out affine transformation on the video monitoring image and the comparison image of each monitoring area based on the preset number of target key point matching pairs;
in the embodiment of the present application, the distance d (g, k1) and d (g, k2) between the vector representation of each keypoint g and the vector representations of two candidate keypoints k1 and k2 are calculated, and a threshold R is selected, if d (g, k1)/d (g, k2) < R, the candidate keypoints k1 and k2 are retained, otherwise, the keypoints k1 and k2 are discarded, wherein a preferred value of the threshold R is 0.8. After the preliminary screening is carried out, a plurality of candidate key point matching pairs exist in the corresponding comparison image of the video monitoring image of each monitoring area, three target key point matching pairs are selected from the candidate key point matching pairs, an affine transformation matrix is calculated based on the three target key point matching pairs, and then the video monitoring image of each monitoring area and the comparison image are subjected to affine transformation.
S2204, performing edge detection and color detection on the video monitoring images of each monitoring area subjected to affine transformation and the comparison image to obtain an edge detection result and a color detection result, and if any one of the video monitoring images of each monitoring area has edge detection abnormality or color detection abnormality, determining that an abnormal condition exists, otherwise, not determining that the abnormal condition exists.
In this embodiment of the application, the performing edge detection and color detection on the video surveillance images of each surveillance area subjected to affine transformation and the comparison images to obtain an edge detection result and a color detection result includes:
dividing the video monitoring image and the comparison image of each monitoring area into M pixel image blocks;
performing edge detection in N directions for each image block in the image blocks of M pixels by M pixels, and calculating an edge distribution histogram of each image block to obtain an N-dimensional histogram vector;
calculating the histogram vector distance between each image block of the video monitoring image of each monitoring area and the corresponding image block in the comparison image;
and if any video monitoring image in the video monitoring images of each monitoring area has an image block of which the distance is greater than a first threshold value, determining that the edge detection of the video monitoring image is abnormal.
In the embodiment of the present application, as shown in fig. 6, the corresponding image blocks refer to image blocks at the same positions in two images, the video surveillance image and the comparison image in each surveillance area are divided into 16 × 16 pixel image blocks, each image block is divided into 4 × 4 pixel sub-image blocks, edge detection is performed on each sub-image block in 8 directions, one direction is taken every 45 °, if a detection value exceeds a threshold T, it is determined that an edge exists in the direction, the edges in the direction are accumulated, an edge distribution histogram of each image block is obtained, and an 8-dimensional histogram vector is obtained at the same time. The first threshold in the embodiment of the present application may be determined according to actual situations, for example: 20. 100, specifically, the following formula is adopted for calculating the histogram vector distance between each image block of the video surveillance image in each surveillance area and the corresponding image block in the comparison image:
wherein D1 represents the histogram vector distance between each image block of the video surveillance image of each surveillance area and the corresponding image block in the comparison image, q1(s) an s-th component, q, of each image block histogram vector of said video surveillance image of each surveillance area2(s) represents the s-th component of the image block histogram vector corresponding to each image block of the video surveillance image of each surveillance area in the comparison image.
The color detection is performed on the video monitoring image of each monitoring area subjected to affine transformation and the comparison image to obtain a color detection result, and the color detection result comprises the following steps:
calculating a color mean value of each image block in the M-by-M pixel image blocks in an RGB color space;
calculating the color distance between each image block of the video monitoring image of each monitoring area and the corresponding image block in the comparison image;
and if any video monitoring image in the video monitoring images of each monitoring area has an image block of which the color distance is greater than a second threshold value, determining that the color detection of the video monitoring image is abnormal.
In the embodiment of the present application, the color distance represents the similarity of colors, and the second threshold may be set according to actual situations, for example: 250 or 4000, etc. The following formula is specifically adopted for calculating the color distance between each image block of the video monitoring image in each monitoring area and the corresponding image block in the comparison image:
wherein D2 represents the color distance between each image block of the video surveillance image of each surveillance area and the corresponding image block in the comparison image, C1,RRepresenting each of said monitoringColor mean value of each image block of video surveillance image of region in R channel, C2,RRepresenting the color mean value of the corresponding image block in the comparison image in the R channel, C1,GA color mean value, C, of each image block of the video surveillance image representing each surveillance area in the G channel2,GRepresenting the color mean value of the corresponding image block in the comparison image in the G channel, C1,BA color mean value, C, of each image block of the video surveillance image representing each surveillance area in the B channel2,BAnd representing the color mean value of the corresponding image block in the comparison image in the B channel.
And S23, if the abnormal situation exists, performing an abnormal situation alarm operation, and acquiring the distance between the position information of the monitoring area where the abnormal situation occurs and the position information of each first target object.
S24, an exception handling task is dispatched to the first target object with the distance smaller than the specified threshold value.
In a specific embodiment of the application, the first target object refers to security personnel or duty personnel in a park, the server reports an abnormal condition to the alarm center for alarming when detecting that any monitoring area of any patrol route has an abnormal condition, specifically, the server intercepts a current image frame of a video monitoring image of the monitoring area with the abnormal condition and a video monitoring image with preset duration, and reports the current image frame, the video monitoring image with the preset duration, current time information and position information of the monitoring area with the abnormal condition to the preset alarm center. Since the terminal of the first target object is installed with a specific application app (application), for example: the cloud city app reports the position information of the first target object to a campus security monitoring center according to a preset position information reporting plan, the alarm center obtains the position information reported by the terminal of the first target object most recently to the current time after receiving the alarm, then calculates the distance between the first target object and the position information of the monitoring area with abnormal conditions, and dispatches an abnormal processing task to the first target object with the distance smaller than a specified threshold value or the shortest distance. Specifically, an exception handling work order may be dispatched to the first target object whose distance is less than the specified threshold, where the work order at least includes location information of the monitoring area where the exception condition exists, for example: and the TCC-A (representing the parking lot area A) receives the first target object of the work order, goes to the site to confirm and process the abnormal condition, and can directly close the work order on an application program of the terminal after the processing is finished, or upload related videos and photos and close the work order after filling related remark instructions.
It can be seen that in the embodiment of the application, when the patrol time arrives, the video monitoring images of a plurality of patrol routes are acquired to execute the preset patrol plan; judging whether an abnormal condition exists in the monitoring area of any one of the patrol routes according to the video monitoring image; if the abnormal condition exists, performing abnormal condition alarm operation, and acquiring the distance between the position information of the monitoring area where the abnormal condition occurs and the position information of each first target object; and dispatching an exception handling task to the first target object with the distance smaller than the specified threshold value, thereby realizing intelligent cloud patrol in the security monitoring center, reducing huge cost overhead caused by requiring numerous human power to participate in patrol and early warning handling, and being beneficial to improving the efficiency of patrol and exception handling in the garden.
Based on the above description, please refer to fig. 7, fig. 7 is a schematic structural diagram of an intelligent patrol and early-warning processing apparatus according to an embodiment of the present application, and as shown in fig. 7, the apparatus includes:
an obtaining module 71, configured to obtain video surveillance images of a plurality of patrol routes when the patrol time arrives to execute a preset patrol plan;
the judging module 72 is configured to judge whether an abnormal condition exists in a monitoring area of any one of the patrol routes according to the video monitoring image;
the processing module 73 is configured to perform an abnormal condition alarm operation if an abnormal condition exists, and acquire a distance between the position information of the monitoring area where the abnormal condition occurs and the position information of each first target object;
the processing module 73 is further configured to dispatch an exception handling task to the first target object whose distance is smaller than the specified threshold.
In one possible implementation manner, each patrol route in the plurality of patrol routes is provided with a plurality of monitoring devices, and each monitoring device in the plurality of monitoring devices is responsible for monitoring a designated monitoring area; the obtaining module 71 is specifically configured to, in obtaining video monitoring images of a plurality of patrol routes to execute a preset patrol plan:
reading the preset patrol plan, wherein the preset patrol plan comprises patrol sequences of the patrol routes;
and acquiring a video monitoring image of each monitoring area of each patrol route in the plurality of patrol routes according to the patrol sequence of the plurality of patrol routes so as to execute a preset patrol plan.
In a possible implementation manner, the determining module 72, in terms of determining whether there is an abnormal condition in the monitoring area of any one of the patrol routes according to the video monitoring image, is specifically configured to:
performing key point detection on the video monitoring image of each monitoring area and a prestored comparison image by adopting a scale invariant feature conversion algorithm, and acquiring a corresponding vector expression, wherein the comparison image is an image of each monitoring area without abnormal conditions;
aiming at each key point of the video monitoring image of each monitoring area, selecting two key points with highest orientation quantity expression similarity from the comparison images to form a candidate key point matching pair;
screening the candidate key point matching pairs to obtain a preset number of target key point matching pairs, and carrying out affine transformation on the video monitoring image and the comparison image of each monitoring area based on the preset number of target key point matching pairs;
and performing edge detection and color detection on the video monitoring images of each monitoring area subjected to affine transformation and the comparison image to obtain an edge detection result and a color detection result, wherein if any one of the video monitoring images of each monitoring area has edge detection abnormality or color detection abnormality, the video monitoring images are considered to have abnormal conditions, otherwise, the video monitoring images are not considered to have abnormal conditions.
In a possible implementation manner, the determining module 72 is specifically configured to, in terms of performing edge detection on the video surveillance image of each surveillance area subjected to affine transformation and the comparison image to obtain an edge detection result:
dividing the video monitoring image and the comparison image of each monitoring area into M pixel image blocks;
performing edge detection in N directions for each image block in the image blocks of M pixels by M pixels, and calculating an edge distribution histogram of each image block to obtain an N-dimensional histogram vector;
calculating the histogram vector distance between each image block of the video monitoring image of each monitoring area and the corresponding image block in the comparison image;
if any video monitoring image in the video monitoring images of each monitoring area has an image block of which the distance is greater than a first threshold value, determining that the edge detection of the video monitoring image is abnormal;
the determining module 72 is specifically configured to perform color detection on the video surveillance image of each surveillance area and the comparison image after affine transformation to obtain a color detection result:
calculating a color mean value of each image block in the M-by-M pixel image blocks in an RGB color space;
calculating the color distance between each image block of the video monitoring image of each monitoring area and the corresponding image block in the comparison image;
and if any video monitoring image in the video monitoring images of each monitoring area has an image block of which the color distance is greater than a second threshold value, determining that the color detection of the video monitoring image is abnormal.
In a possible implementation, the processing module 73 is further configured to: and in the process of executing the patrol plan, randomly popping up patrol check-in windows on video monitoring image display interfaces corresponding to a plurality of monitoring devices of the current patrol route, so that a second target object is checked in on-line and patrol records are generated.
In a possible implementation manner, the processing module 73 is specifically configured to, in terms of performing an abnormal situation warning operation:
intercepting a current image frame of a video monitoring image of a monitoring area with an abnormal condition and a video monitoring image with preset duration;
and reporting the current image frame, the video monitoring image with the preset duration, the current time information and the position information of the monitoring area with the abnormal condition to a preset alarm center for alarming.
In a possible implementation, the processing module 73 is further configured to: and receiving the position information reported by the terminal of the first target object when the terminal arrives at the preset time interval.
According to the embodiment of the application, when the patrol time is up, the video monitoring images of a plurality of patrol routes are obtained to execute the preset patrol plan; judging whether an abnormal condition exists in the monitoring area of any one of the patrol routes according to the video monitoring image; if the abnormal condition exists, performing abnormal condition alarm operation, and acquiring the distance between the position information of the monitoring area where the abnormal condition occurs and the position information of each first target object; and dispatching an exception handling task to the first target object with the distance smaller than the specified threshold value, thereby realizing intelligent cloud patrol in the security monitoring center, reducing huge cost overhead caused by requiring numerous human power to participate in patrol and early warning handling, and being beneficial to improving the efficiency of patrol and exception handling in the garden.
It should be noted that the intelligent patrol and early-warning processing device provided in the embodiment of the present application can implement the steps in the intelligent patrol and early-warning processing method shown in fig. 2, and can achieve the same or similar beneficial effects, and the device can be applied to an actual intelligent patrol scene.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, as shown in fig. 8, including: a memory 81 for storing a computer program; a processor 82, configured to call a computer program stored in the memory 81 to implement the steps in the embodiment of the intelligent patrol and early warning processing method; at least one output interface 83 for outputting; at least one input interface 84 for input; the various components are connected to at least one bus to enable communication.
The processor 82 is specifically configured to invoke a computer program to execute the following steps:
when the patrol time is up, acquiring video monitoring images of a plurality of patrol routes to execute a preset patrol plan;
judging whether an abnormal condition exists in the monitoring area of any one of the patrol routes according to the video monitoring image;
if the abnormal condition exists, performing abnormal condition alarm operation, and acquiring the distance between the position information of the monitoring area where the abnormal condition occurs and the position information of each first target object;
and dispatching an exception handling task to the first target object with the distance smaller than a specified threshold value.
Optionally, each patrol route in the plurality of patrol routes is provided with a plurality of monitoring devices, and each monitoring device in the plurality of monitoring devices is responsible for monitoring a specified monitoring area; the processor 82 executes the acquiring of the video surveillance images of the plurality of patrol routes to execute a preset patrol plan, including:
reading the preset patrol plan, wherein the preset patrol plan comprises patrol sequences of the patrol routes;
and acquiring a video monitoring image of each monitoring area of each patrol route in the plurality of patrol routes according to the patrol sequence of the plurality of patrol routes so as to execute a preset patrol plan.
Optionally, the determining, by the processor 82, whether an abnormal condition exists in a monitoring area of any one of the patrol routes according to the video monitoring image includes:
performing key point detection on the video monitoring image of each monitoring area and a prestored comparison image by adopting a scale invariant feature conversion algorithm, and acquiring a corresponding vector expression, wherein the comparison image is an image of each monitoring area without abnormal conditions;
aiming at each key point of the video monitoring image of each monitoring area, selecting two key points with highest orientation quantity expression similarity from the comparison images to form a candidate key point matching pair;
screening the candidate key point matching pairs to obtain a preset number of target key point matching pairs, and carrying out affine transformation on the video monitoring image and the comparison image of each monitoring area based on the preset number of target key point matching pairs;
and performing edge detection and color detection on the video monitoring images of each monitoring area subjected to affine transformation and the comparison image to obtain an edge detection result and a color detection result, wherein if any one of the video monitoring images of each monitoring area has edge detection abnormality or color detection abnormality, the video monitoring images are considered to have abnormal conditions, otherwise, the video monitoring images are not considered to have abnormal conditions.
Optionally, the processor 82 performs edge detection on the video surveillance image of each surveillance area subjected to affine transformation and the comparison image to obtain an edge detection result, where the edge detection result includes:
dividing the video monitoring image and the comparison image of each monitoring area into M pixel image blocks;
performing edge detection in N directions for each image block in the image blocks of M pixels by M pixels, and calculating an edge distribution histogram of each image block to obtain an N-dimensional histogram vector;
calculating the histogram vector distance between each image block of the video monitoring image of each monitoring area and the corresponding image block in the comparison image;
if any video monitoring image in the video monitoring images of each monitoring area has an image block of which the distance is greater than a first threshold value, determining that the edge detection of the video monitoring image is abnormal;
the processor 82 performs the color detection on the video monitoring image of each monitoring area subjected to affine transformation and the comparison image to obtain a color detection result, including:
calculating a color mean value of each image block in the M-by-M pixel image blocks in an RGB color space;
calculating the color distance between each image block of the video monitoring image of each monitoring area and the corresponding image block in the comparison image;
and if any video monitoring image in the video monitoring images of each monitoring area has an image block of which the color distance is greater than a second threshold value, determining that the color detection of the video monitoring image is abnormal.
Optionally, the processor 82 is further configured to: and popping up patrol check-in windows at the video monitoring image display interfaces corresponding to the plurality of monitoring devices of the current patrol route randomly, so that the second target object is checked in on line, and patrol records are generated.
Optionally, the processor 82 executes the abnormal condition warning operation, including:
intercepting a current image frame of a video monitoring image of a monitoring area with an abnormal condition and a video monitoring image with preset duration;
and reporting the current image frame, the video monitoring image with the preset duration, the current time information and the position information of the monitoring area with the abnormal condition to a preset alarm center for alarming.
Optionally, the processor 82 is further configured to:
and receiving the position information reported by the terminal of the first target object when the terminal arrives at the preset time interval.
According to the embodiment of the application, when the patrol time is up, the video monitoring images of a plurality of patrol routes are obtained to execute the preset patrol plan; judging whether an abnormal condition exists in the monitoring area of any one of the patrol routes according to the video monitoring image; if the abnormal condition exists, performing abnormal condition alarm operation, and acquiring the distance between the position information of the monitoring area where the abnormal condition occurs and the position information of each first target object; and dispatching an exception handling task to the first target object with the distance smaller than the specified threshold value, thereby realizing intelligent cloud patrol in the security monitoring center, reducing huge cost overhead caused by requiring numerous human power to participate in patrol and early warning handling, and being beneficial to improving the efficiency of patrol and exception handling in the garden.
Illustratively, the electronic device may be a computer, a notebook computer, a tablet computer, a palm computer, a server, a cloud server, and the like. The electronic devices may include, but are not limited to, a processor 82, a memory 81, an input output interface 83, and a bus 84. It will be appreciated by those skilled in the art that the schematic diagrams are merely examples of an electronic device and are not limiting of an electronic device and may include more or fewer components than those shown, or some components in combination, or different components.
It should be noted that, since the steps in the intelligent patrol and early-warning processing method are implemented when the processor 82 of the electronic device executes the computer program, the embodiments or implementations of the intelligent patrol and early-warning processing method are all applicable to the electronic device, and all can achieve the same or similar beneficial effects.
The embodiment of the application also provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps in the intelligent patrol and early warning processing method.
Illustratively, the computer program of the computer-readable storage medium comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
It should be noted that, since the computer program of the computer-readable storage medium is executed by the processor to implement the steps of the intelligent patrol and early-warning processing method, all the embodiments or implementations of the intelligent patrol and early-warning processing method are applicable to the computer-readable storage medium, and can achieve the same or similar beneficial effects.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. An intelligent patrol and early warning processing method is characterized by comprising the following steps:
when the patrol time is up, acquiring video monitoring images of a plurality of patrol routes to execute a preset patrol plan;
judging whether an abnormal condition exists in the monitoring area of any one of the patrol routes according to the video monitoring image;
if the abnormal condition exists, performing abnormal condition alarm operation, and acquiring the distance between the position information of the monitoring area where the abnormal condition occurs and the position information of each first target object;
and dispatching an exception handling task to the first target object with the distance smaller than a specified threshold value.
2. The method according to claim 1, wherein each of the plurality of patrol routes is provided with a plurality of monitoring devices, and each of the plurality of monitoring devices is responsible for monitoring a designated monitoring area; the acquiring of the video monitoring images of the plurality of patrol routes to execute the preset patrol plan includes:
reading the preset patrol plan, wherein the preset patrol plan comprises patrol sequences of the patrol routes;
and acquiring a video monitoring image of each monitoring area of each patrol route in the plurality of patrol routes according to the patrol sequence of the plurality of patrol routes so as to execute a preset patrol plan.
3. The method according to claim 2, wherein the determining whether an abnormal condition exists in a monitoring area of any one of the patrol routes according to the video monitoring image comprises:
performing key point detection on the video monitoring image of each monitoring area and a prestored comparison image by adopting a scale invariant feature conversion algorithm, and acquiring a corresponding vector expression, wherein the comparison image is an image of each monitoring area without abnormal conditions;
aiming at each key point of the video monitoring image of each monitoring area, selecting two key points with highest orientation quantity expression similarity from the comparison images to form a candidate key point matching pair;
screening the candidate key point matching pairs to obtain a preset number of target key point matching pairs, and carrying out affine transformation on the video monitoring image and the comparison image of each monitoring area based on the preset number of target key point matching pairs;
and performing edge detection and color detection on the video monitoring images of each monitoring area subjected to affine transformation and the comparison image to obtain an edge detection result and a color detection result, wherein if any one of the video monitoring images of each monitoring area has edge detection abnormality or color detection abnormality, the video monitoring images are considered to have abnormal conditions, otherwise, the video monitoring images are not considered to have abnormal conditions.
4. The method according to claim 3, wherein the performing edge detection on the affine-transformed video surveillance image and the comparison image of each surveillance area to obtain an edge detection result comprises:
dividing the video monitoring image and the comparison image of each monitoring area into M pixel image blocks;
performing edge detection in N directions for each image block in the image blocks of M pixels by M pixels, and calculating an edge distribution histogram of each image block to obtain an N-dimensional histogram vector;
calculating the histogram vector distance between each image block of the video monitoring image of each monitoring area and the corresponding image block in the comparison image;
if any video monitoring image in the video monitoring images of each monitoring area has an image block of which the distance is greater than a first threshold value, determining that the edge detection of the video monitoring image is abnormal;
the color detection is performed on the video monitoring image of each monitoring area subjected to affine transformation and the comparison image to obtain a color detection result, and the color detection result comprises the following steps:
calculating a color mean value of each image block in the M-by-M pixel image blocks in an RGB color space;
calculating the color distance between each image block of the video monitoring image of each monitoring area and the corresponding image block in the comparison image;
and if any video monitoring image in the video monitoring images of each monitoring area has an image block of which the color distance is greater than a second threshold value, determining that the color detection of the video monitoring image is abnormal.
5. The method of claim 2, further comprising:
and in the process of executing the patrol plan, randomly popping up patrol check-in windows on video monitoring image display interfaces corresponding to a plurality of monitoring devices of the current patrol route, so that a second target object is checked in on-line and patrol records are generated.
6. The method of claim 1, wherein said performing an abnormal situation alert operation comprises:
intercepting a current image frame of a video monitoring image of a monitoring area with an abnormal condition and a video monitoring image with preset duration;
and reporting the current image frame, the video monitoring image with the preset duration, the current time information and the position information of the monitoring area with the abnormal condition to a preset alarm center for alarming.
7. The method of claim 1, further comprising:
and receiving the position information reported by the terminal of the first target object when the terminal arrives at the preset time interval.
8. An intelligent patrol and early warning processing device, which is characterized in that the device comprises:
the acquisition module is used for acquiring video monitoring images of a plurality of patrol routes to execute a preset patrol plan when the patrol time arrives;
the judging module is used for judging whether an abnormal condition exists in a monitoring area of any patrol route in the plurality of patrol routes according to the video monitoring image;
the processing module is used for carrying out abnormal condition alarming operation if abnormal conditions exist, and acquiring the distance between the position information of the monitoring area where the abnormal conditions occur and the position information of each first target object;
the processing module is further used for dispatching an exception handling task to the first target object with the distance smaller than the specified threshold value.
9. An electronic device comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the intelligent patrol and early warning processing method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the intelligent patrol and early warning processing method according to any one of claims 1 to 7.
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Application publication date: 20200103 |