CN117915138A - Video time calibration method in data center station - Google Patents
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Abstract
The invention provides a video time calibration method in a data center station, which relates to the technical field of video calibration and comprises the following steps: acquiring a monitoring video in monitoring equipment, and forming an equipment included angle data set according to the monitoring video; according to a calculation formula of the angular velocity, calculating to obtain the angular velocity in the equipment included angle data set, screening to obtain a first equipment set to be calibrated according to the angular velocity, and screening the first equipment set to be calibrated by using a screening algorithm to obtain a second equipment set to be calibrated; traversing the second equipment set to be calibrated, and calculating to obtain a time deviation set of the second equipment set to be calibrated; and carrying out time calibration on the second equipment set to be calibrated according to the time deviation set to obtain the calibrated monitoring equipment. According to the invention, the angle between the sun and the area is determined by analyzing the shadow of a person or object in the video, and the historical video time in the device is calibrated according to the angle deviation, so that the accuracy of time sequence analysis can be ensured.
Description
Technical Field
The invention relates to the technical field of video calibration, in particular to a video time calibration method in a data center station.
Background
In digital rural areas, video monitoring devices are deployed outdoors and many are deployed in the same area. In order to save cost, no special timing server is provided for the monitoring devices, and a small part of time lag or lead of the monitoring devices is easy to occur, and the time difference is less than a few hours. The collection of these devices in the data center generates a large amount of time stamped historical video data, possibly with the clocks of the devices already calibrated, but the time in the generated historical video not. In timing analysis of structured data in these videos, it is necessary to ensure accuracy of video time. For this reason, a method is needed to correct the temporal problem in a small portion of the video using the information implicit in the video.
The invention patent with the Chinese application number 201310370996.0 discloses a device and a method for calibrating monitoring video time, wherein the device comprises a target frame acquisition module, a target time calibration module and a video time calibration module. Randomly selecting a video frame with a frame sequence n from the monitoring video as a target frame; acquiring target time t 1 from the target frame picture through analysis and automatic identification of the monitoring video; calculating the real time t 2 of the target frame according to the known time deviation dt, namely the time difference between the target time and the ideal time, namely t 2＝t1 +dt, and the target time t 1; and analyzing the monitoring video to obtain the frame rate f, and calculating the real time corresponding to the video frame with any frame sequence m to realize the time calibration of the whole monitoring video. The prior art performs time calibration by manually reading standard time, has high labor cost and limited calibration maintenance time, and cannot be applied to a large-scale monitoring video for 24-hour long-term monitoring.
Disclosure of Invention
In view of the above, the present invention provides a method for calibrating video time in a data center, which can determine the angle between the sun and the area by analyzing the shadow of a person or object in the video in sunny weather, and calibrate the historical video time in the device according to the angle deviation, so as to ensure the accuracy of time sequence analysis.
The technical purpose of the invention is realized as follows:
The invention provides a video time calibration method in a data center station, which comprises the following steps:
S1, acquiring a monitoring video in monitoring equipment, and calculating the included angle between the sun and the ground under each monitoring equipment by using an included angle calculation method according to the monitoring video to form an equipment included angle data set, wherein the equipment included angle data set comprises the included angle under the monitoring equipment, a timestamp of the monitoring equipment and an equipment number;
S2, calculating the angular velocity in the equipment included angle data set according to a calculation formula of the angular velocity, screening according to the angular velocity to obtain a first equipment set to be calibrated, and screening the first equipment to be calibrated by using a screening algorithm to obtain a second equipment set to be calibrated;
s3, traversing a second equipment set to be calibrated, and calculating to obtain a time deviation set of the second equipment set to be calibrated;
and S4, performing time calibration on the second equipment set to be calibrated according to the time deviation set to obtain the calibrated monitoring equipment.
Based on the above technical solution, preferably, step S1 includes:
S11, acquiring historical weather data, screening out the date of sunny weather from the historical weather data, acquiring corresponding monitoring videos according to the date of sunny weather, filtering the monitoring videos by adopting a processing algorithm, only reserving daytime videos, and acquiring monitoring equipment to be detected according to the source of the daytime videos, wherein each monitoring equipment to be detected comprises equipment numbers;
S12, selecting a monitoring device to be detected as a target device, and arranging daytime videos in the target device according to a time sequence to form a video sequence;
s13, selecting a daytime video from the video sequence as a target video;
S14, selecting one image frame in the target video as a target image frame;
S15, identifying a target image frame by using a machine vision method, if the shadow and the timestamp of the image frame are identified, entering a step S16, otherwise, returning to the step S14 to reselect one image frame as the target image frame;
S16, measuring the height of a person or an object and the length of a shadow by using a machine vision algorithm, expressing the measured height and length by pixel values, substituting the pixel values of the height and the length into an included angle calculation formula for calculation, and obtaining the included angle between the sun and the ground;
S17, judging whether the included angle is in a threshold range, if so, correlating the included angle, the timestamp of the target image frame and the equipment number, outputting the correlated included angle, the timestamp and the equipment number as equipment included angle data of target equipment, returning to the step S12, selecting another monitoring equipment as the target equipment, and if not, removing the image frame from the target video and returning to the step S14;
S18, repeating the steps S12-S17 until all the monitoring devices to be detected are selected, and outputting to obtain a device included angle data set.
Based on the above technical scheme, preferably, the calculation formula of the included angle is:
Where h is the pixel value of the height; l is the pixel value of the length; b is the included angle between the monitoring interface and the horizontal plane in the target image frame; determining an included angle according to the direction of the shadow relative to the person or object:
When the shadow is positioned on the west side of a person or an object, the included angle is a;
when the shadow is positioned at the east side of a person or an object, the included angle is
Based on the above technical solution, preferably, in step S17, the threshold range is:
when the included angle is a, the threshold range is [30 degrees, 80 degrees ];
When the included angle is The threshold range is 110, 150.
Based on the above technical solution, preferably, step S2 includes:
S21, taking a timestamp in the equipment included angle data set as a measurement point of each equipment included angle data, determining the angular velocity between measurement points by utilizing an angular velocity calculation formula according to the uniform change rule of the equipment included angle data set based on the included angle, acquiring a reference angular velocity, and identifying a monitoring equipment set with accurate clock running according to the difference between the calculated value of the angular velocity and the reference angular velocity, wherein monitoring equipment not in the set is used as a first equipment set to be calibrated;
S22, taking an included angle of each first device to be calibrated in the first device set to be calibrated as a first included angle value, selecting a value with the largest included angle from a monitoring device set with accurate clock operation as a second included angle value, taking a monitoring device corresponding to the second included angle value as a second device, taking the second device as a reference, calculating a to-be-calibrated angular velocity between each first device to be calibrated and the second device according to the first included angle value, the second included angle value, a timestamp of the first device to be calibrated and a timestamp of the second device, judging whether the clock of the first device to be calibrated is accurate or not according to the difference between the to-be-calibrated angular velocity and the reference angular velocity, and taking the first device to be calibrated with a clock problem as the second device set to be calibrated.
On the basis of the above technical solution, preferably, step S21 includes:
S211, sorting the equipment included angle data sets according to the included angles from small to large to obtain a first sequence table, wherein each element in the first sequence table comprises an included angle, a time stamp and an equipment number; unidirectional directional links are carried out on the elements in the first sequence table according to the adjacent sequence, each element points to the next adjacent element, and the last element points to the first element, so that a cyclic list is constructed;
S212, setting element interval numbers through a cyclic list, and calculating the angular velocity among elements by using an angular velocity formula according to the element interval numbers; generating an angular velocity sequence according to the angular velocity calculated by the cyclic list, wherein one record in the angular velocity sequence comprises the angular velocity and the associated initial equipment number and the associated termination equipment number;
S213 sets an abnormal angular velocity set N, initially n=0;
S214, calculating the average value of all angular velocities in the angular velocity sequence, and taking the average value as a reference angular velocity;
S215, selecting one angular velocity from the angular velocity sequence as a target angular velocity, calculating a difference value between the target angular velocity and a reference angular velocity, judging that the target angular velocity is accurate if the difference value is smaller than an error threshold value, and adding the target angular velocity into an abnormal angular velocity set if the difference value is larger than or equal to the error threshold value, so that N=N+1;
S216, repeating the step S215, and traversing all angular velocities in the angular velocity sequence to obtain a traversed abnormal angular velocity set N;
s217 determines whether N is greater than 0:
If N >0, then step S218 is performed;
If n=0, step S219 is executed;
s218, eliminating abnormal angular velocity from the angular velocity sequence, updating the angular velocity sequence, and returning to the step S214 to recalculate the reference angular velocity;
S219, eliminating repeated records in the angular velocity sequence to obtain a final angular velocity sequence and a reference angular velocity, performing overlapping comparison according to the equipment number of the final angular velocity sequence and the equipment number of the equipment included angle data set, taking the monitoring equipment corresponding to the overlapped equipment number as a monitoring equipment set with accurate clock operation, and taking the rest monitoring equipment as a first equipment set to be calibrated.
On the basis of the above technical solution, preferably, step S22 includes:
S221, selecting a current first device to be calibrated from the first device to be calibrated set, and acquiring a corresponding first included angle value;
S222, selecting a value with the largest included angle from a monitoring equipment set with accurate clock operation as a second included angle value, and calculating the undetermined angular velocity according to an angular velocity calculation formula;
s223 judges the difference between the pending angular velocity and the reference angular velocity:
If the difference value is larger than the error threshold value, judging the current first equipment to be calibrated as problem equipment, and classifying the problem equipment into a second equipment set to be calibrated;
if the difference value is smaller than or equal to the error threshold value, judging that the current first equipment to be calibrated is normal equipment, and classifying the first equipment to be calibrated into a normal equipment set;
S224, repeating the steps S221-S223, traversing all first equipment to be calibrated in the first equipment to be calibrated to obtain a second equipment set to be calibrated and a normal equipment set, and calculating constant angular velocity according to the normal equipment set.
Based on the above technical solution, preferably, step S3 includes:
S31, selecting a current second device to be calibrated from the second device set to be calibrated;
s32, initializing two variables theta and ID, wherein theta is set to be pi, and the theta is used for recording the maximum included angle difference value; an ID set to 0 for recording the device number of the normal device;
S33, selecting a current normal device from the normal device set, and calculating an included angle difference value between an included angle of the current normal device and an included angle of the current second device to be calibrated;
S34, comparing the included angle difference value with a variable theta:
If the included angle difference value > theta, updating the variable theta to be the included angle difference value, and recording the ID of the current normal equipment;
If the included angle difference value is less than or equal to theta, returning to the step S33 to reselect a current normal device;
S35, repeating the steps S33-S34, traversing all normal devices in the normal device set, and obtaining finally updated theta and ID;
s36, calculating the time deviation delta T of the current second equipment to be calibrated according to the finally updated theta, and recording the equipment number and the time deviation delta T of the current second equipment to be calibrated into a time deviation set;
S37, repeating the steps S31-S36, traversing all the second devices to be calibrated in the second device set to be calibrated, and outputting to obtain a final time deviation set, wherein each record contains a device number and a corresponding time deviation delta T in the time deviation set.
Based on the above technical solution, preferably, the calculation formula of the time deviation is:
Wherein Δt is a time deviation, w 0 is a constant angular velocity, T 2 is a time stamp of a normal device, T 1 is a time stamp of a second device to be calibrated, θ is an included angle difference value, θ=a 1-a2,a1 is an included angle of the second device to be calibrated, and a 2 is an included angle of the normal device.
Based on the above technical solution, preferably, step S4 includes:
S41, selecting one piece of current time calibration data from the time deviation set, and searching all monitoring videos corresponding to equipment according to equipment numbers in the current time calibration data to serve as videos to be calibrated;
S42, performing time calibration on the video to be calibrated according to the time deviation delta T in the current time calibration data, and adjusting the time stamp of the video to be calibrated to obtain a calibrated video;
s43, repeating the steps S41-S42, traversing all time calibration data in the time deviation set, and completing time calibration for all videos to be calibrated.
Compared with the prior art, the method has the following beneficial effects:
(1) The method provided by the invention can realize high-precision time calibration of the monitoring equipment through the included angle calculation method and the angular velocity calculation formula, automatically screen the equipment to be calibrated through the algorithm, calculate a time deviation set, and perform time calibration on the equipment, thereby reducing manual intervention, improving efficiency, realizing time calibration on a plurality of equipment through calculating the included angle between the sun and the ground under each monitoring equipment, improving the application range, and realizing time calibration on the real-time monitoring equipment through the angular velocity calculation and screening algorithm, and ensuring the real-time property and accuracy of monitoring data;
(2) According to the invention, the quality and the continuity of the monitoring video are improved by screening the sunny weather date and arranging the video sequence; the shadow and the time stamp in the target image frame are identified by using a machine vision method, and the information required by the included angle between the sun and the ground is automatically acquired, so that the manual intervention is reduced; through the calculation of the included angle and the judgment of the threshold value, the automatic measurement and the data screening of the included angle between the sun and the ground are realized, and the accuracy and the reliability of the included angle data set of the equipment are ensured;
(3) The invention realizes the automatic evaluation and calibration of the clock accuracy of the monitoring equipment by identifying and processing the abnormal angular velocity and comparing and calculating the included angle data set of the equipment, which is beneficial to improving the reliability and accuracy of the data and ensuring the clock accuracy and stability of the monitoring equipment;
(4) The invention realizes the calculation and recording of the time deviation through the traversal and comparison of the normal equipment set and the second equipment set to be calibrated. This helps to determine the time accuracy of each device and record the time offset into a set of time offsets, providing accurate reference data for subsequent time calibration.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method according to an embodiment of the invention.
FIG. 2 is a schematic diagram of a process for forming a device angle dataset according to an embodiment of the present invention;
FIG. 3 is a schematic view illustrating the angle between the sun and the ground according to an embodiment of the present invention;
Fig. 4 is a schematic diagram of a screening process of a second device set to be calibrated according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of forming a time offset set according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a time alignment process according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
As shown in fig. 1, the present invention provides a method for calibrating video time in a data center station, comprising the steps of:
S1, acquiring a monitoring video in monitoring equipment, and calculating the included angle between the sun and the ground under each monitoring equipment by using an included angle calculation method according to the monitoring video to form an equipment included angle data set, wherein the equipment included angle data set comprises the included angle under the monitoring equipment, a timestamp of the monitoring equipment and an equipment number;
S2, calculating the angular velocity in the equipment included angle data set according to a calculation formula of the angular velocity, screening according to the angular velocity to obtain a first equipment set to be calibrated, and screening the first equipment to be calibrated by using a screening algorithm to obtain a second equipment set to be calibrated;
s3, traversing a second equipment set to be calibrated, and calculating to obtain a time deviation set of the second equipment set to be calibrated;
and S4, performing time calibration on the second equipment set to be calibrated according to the time deviation set to obtain the calibrated monitoring equipment.
In a small area, such as a village or county, the angle of the sun's trajectory in the sky relative to the ground varies evenly throughout the day. With this law, under sunny weather conditions, the angle of the sun relative to the ground at a particular moment can be measured by video capturing a person or object and its shadow on the ground. This angle varies evenly over time and at the same time the sun angles at different locations are consistent.
In order to achieve the aim, a sunny day is selected, people or objects and shadows thereof in the video are identified from the historical video by using a machine vision technology, and therefore the sun angle at a specific moment is calculated. And selecting any angle information of all devices in the area at different time points, and arranging the angles in a sequence from small to large, so that the angular speed between adjacent time points can be calculated.
If the time of the device is accurate, the calculated angular velocity between adjacent sequences should be the same. If a device is found to have significant differences in angular velocity from other devices, it can be inferred that there may be an anomaly in the clock of the device. By this method, inaccuracy in the clock settings of those devices is effectively detected, and the devices that need to be time-calibrated are determined.
By observing the person or object in the video and its reflection, the angle between the sun and the ground is calculated first, which effectively implies time information. Then find out those video devices which do not meet the rules by combining the accurate time of most devices. The time offset is then calculated for the devices whose clocks are in question, and finally the devices are time-aligned with this time offset to achieve accurate time synchronization. In summary: using the relationship between solar angle and time, the time offset is inferred by analyzing the shadow in the video, and the video produced in the device is time aligned.
Calculating the included angle between the sun and the ground:
during operation of the outdoor monitoring device, the device will constantly capture real-time pictures within its monitoring range. In sunny weather conditions, the monitoring device can record not only the person or object itself, but also its projection on the ground. If a person or an object and a corresponding projection thereof appear in the monitoring picture, the angle between the sun and the ground is determined by measuring the ratio of the actual height of the object to the projection length thereof. This calculation also requires consideration of the specific orientation of the monitoring device.
Because of the different locations of the monitoring devices, the specific points in time at which they capture the picture containing the person or object and its projection may be different. The time watermark in each monitor screen provides the time for the device to determine the angle of the sun to the ground.
By the method, at different time points, the included angles between the sun and the ground at the respective positions and the time of the equipment corresponding to the included angles are calculated by using the data captured by all monitoring equipment.
Specifically, in an embodiment of the present invention, step S1 includes:
S11, acquiring historical weather data, screening out the date of sunny weather from the historical weather data, acquiring corresponding monitoring videos according to the date of sunny weather, filtering the monitoring videos by adopting a processing algorithm, only reserving daytime videos, and acquiring monitoring equipment to be detected according to the source of the daytime videos, wherein each monitoring equipment to be detected comprises equipment numbers;
S12, selecting a monitoring device to be detected as a target device, and arranging daytime videos in the target device according to a time sequence to form a video sequence;
s13, selecting a daytime video from the video sequence as a target video;
S14, selecting one image frame in the target video as a target image frame;
S15, identifying a target image frame by using a machine vision method, if the shadow and the timestamp of the image frame are identified, entering a step S16, otherwise, returning to the step S14 to reselect one image frame as the target image frame;
S16, measuring the height of a person or an object and the length of a shadow by using a machine vision algorithm, expressing the measured height and length by pixel values, substituting the pixel values of the height and the length into an included angle calculation formula for calculation, and obtaining the included angle between the sun and the ground;
S17, judging whether the included angle is in a threshold range, if so, correlating the included angle, the timestamp of the target image frame and the equipment number, outputting the correlated included angle, the timestamp and the equipment number as equipment included angle data of target equipment, returning to the step S12, selecting another monitoring equipment as the target equipment, and if not, removing the image frame from the target video and returning to the step S14;
S18, repeating the steps S12-S17 until all the monitoring devices to be detected are selected, and outputting to obtain a device included angle data set.
Referring to fig. 2, a specific example is described as follows:
Step 1: selecting a date of sunny weather: and selecting out the sunny weather date from the historical weather data. It is ensured that during the day of the day the monitoring device can clearly capture information of people or objects and their shadows. After the selection is completed, step 2 is performed.
Step 2: filtering video data: and screening out monitoring videos corresponding to the sunny weather date, and excluding videos of other dates. And (3) further filtering by using a video processing algorithm, removing all night videos, and only reserving the daytime videos. After the filtration is completed, step 3 is entered.
Step 3: selecting monitoring equipment: and (3) classifying the videos according to the monitoring equipment according to the result of the step (2). The video of each device constitutes a category. And selecting the next device which does not perform included angle calculation according to the device numbering sequence. If the operation is performed for the first time, the device with the smallest number is selected. After completion, step 4 is entered.
Step 4: selecting a video file: a file is selected from the video generated by the selected device. In general, videos of the same day are chronologically arranged in a data center, divided into a plurality of files, and one of them is chronologically selected. After the selection is completed, the process proceeds to step 5.
Step 5: selecting a video frame: in the selected video, a particular frame is selected. Video is made up of a plurality of successive frames. If the first selection is made, selecting a first frame; if not, the next frame immediately preceding is selected. After the selection is completed, step 6 is entered.
Step 6: judging people or things and shadows thereof: in the frame selected in step 5, machine vision techniques are used to determine whether a person or a particular object is present (typically, the object is particular, such as a stake standing vertically on the ground at the point of monitoring) and whether there is a corresponding shadow. If people or objects and shadows thereof exist, and the time watermark on the frame can be identified, the step 9 is entered; otherwise, step 7 is entered.
Step 7: check whether all frames are traversed: it is determined whether all frames in the video have been checked. If yes, the step 8 is needed to be entered, wherein the step indicates that no frame meeting the condition is found in the video; if not, returning to the step 5, and continuing to check the next frame.
Step 8: check whether all videos are traversed: it is determined whether all videos of the selected device have been checked and it is confirmed that no eligible frames exist. If yes, the device is indicated to have no data available for calculation in the selected date, and the step 3 is needed to be returned to select another device; if not, go back to step 4 to select the next video for inspection.
Step 9: calculating an included angle: first, the height of a person or object and the length of a shadow are measured using a machine vision algorithm and expressed in pixel values. Then, the following formula is applied to calculate an included angle a, and a schematic diagram of the included angle is shown in fig. 3:
Where h is the pixel value of the height; l is the pixel value of the length; b is the angle between the monitoring interface and the horizontal plane in the target image frame (b is 0 degrees if the monitoring interface is horizontal).
Considering that the included angle is 0 degree when the sun rises and 180 degrees when the sun falls. Determining the solar included angle according to the direction of the shadow corresponding to a person or object:
When the shadow is positioned on the west side of a person or an object, the included angle is a;
when the shadow is positioned at the east side of a person or an object, the included angle is
After determining the angle between the sun and the ground, step 10 is entered.
Step 10: judging whether the included angle is proper or not: taking into account that errors may affect the measurement, especially when the sun is just rising or approaching noon. When the sun is just lifted, the shadow of a person or object is long and even exceeds the coverage range of a monitoring picture, and the result is inaccurate after the shadow is identified by an algorithm. When the person or object is in the middle of the day, the shadow of the person or object is very small, and the shadow recognition error enlarges the calculation result, so that it is required to verify whether the calculated included angle is in a reasonable range between 30 degrees and 80 degrees or between 110 degrees and 150 degrees. If yes, continuing to step 11; otherwise, another time point needs to be reselected for measurement, and the process proceeds to step 7.
Step 11: recording data: and (3) correlating and recording the calculated solar included angle, the timestamp of the corresponding frame and the equipment number, wherein each equipment has only one record. After completion, step 12 is entered.
Step 12: check if all devices are traversed: confirm whether all devices have completed the above procedure. If yes, go to step 13; otherwise, returning to the step 3 to continue to operate the unprocessed equipment.
Step 13: generating a data set: the data recorded by all the devices in the step 11 are summarized to form a complete device included angle data set, and each device has only one included angle. For subsequent analysis and processing.
Device for determining clock problem
The device angle data set collected in the first step is based on a uniform law of variation of the solar angle. And determining the angular velocity between each measuring point in the data set by applying a calculation formula of the angular velocity. If the clock of the device fails, the angular velocity it calculates will be significantly different from the angular velocity of the device that is operating the clock normally. Based on this principle, the reference angular velocity w 1 of the region is first calculated, and a set of devices whose clocks are running accurately is identified accordingly, whereas devices not in this set are regarded as devices suspected of having clock problems.
Further, a device suspected of being problematic is selected, and the included angle value is recorded. And then, finding out the device with the largest included angle value from the accurate device set of the clock. The angular velocity is calculated by comparing the difference in the angle between the two devices with the time difference. If the angular velocity of the suspected problematic device does not differ much from the area reference angular velocity w 1, the clock of the device can be considered accurate; otherwise, it is ascertained that the clock of the device does have a problem, and time calibration is required.
Specifically, in an embodiment of the present invention, step S2 includes:
S21, taking a timestamp in the equipment included angle data set as a measurement point of each equipment included angle data, determining the angular velocity between measurement points by utilizing an angular velocity calculation formula according to the uniform change rule of the equipment included angle data set based on the included angle, acquiring a reference angular velocity, and identifying a monitoring equipment set with accurate clock running according to the difference between the calculated value of the angular velocity and the reference angular velocity, wherein monitoring equipment not in the set is used as a first equipment set to be calibrated;
S22, taking an included angle of each first device to be calibrated in the first device set to be calibrated as a first included angle value, selecting a value with the largest included angle from a monitoring device set with accurate clock operation as a second included angle value, taking a monitoring device corresponding to the second included angle value as a second device, taking the second device as a reference, calculating a to-be-calibrated angular velocity between each first device to be calibrated and the second device according to the first included angle value, the second included angle value, a timestamp of the first device to be calibrated and a timestamp of the second device, judging whether the clock of the first device to be calibrated is accurate or not according to the difference between the to-be-calibrated angular velocity and the reference angular velocity, and taking the first device to be calibrated with a clock problem as the second device set to be calibrated.
In this embodiment, step S21 includes:
S211, sorting the equipment included angle data sets according to the included angles from small to large to obtain a first sequence table, wherein each element in the first sequence table comprises an included angle, a time stamp and an equipment number; unidirectional directional links are carried out on the elements in the first sequence table according to the adjacent sequence, each element points to the next adjacent element, and the last element points to the first element, so that a cyclic list is constructed;
S212, setting element interval numbers through a cyclic list, and calculating the angular velocity among elements by using an angular velocity formula according to the element interval numbers; generating an angular velocity sequence according to the angular velocity calculated by the cyclic list, wherein one record in the angular velocity sequence comprises the angular velocity and the associated initial equipment number and the associated termination equipment number;
S213 sets an abnormal angular velocity set N, initially n=0;
S214, calculating the average value of all angular velocities in the angular velocity sequence, and taking the average value as a reference angular velocity;
S215, selecting one angular velocity from the angular velocity sequence as a target angular velocity, calculating a difference value between the target angular velocity and a reference angular velocity, judging that the target angular velocity is accurate if the difference value is smaller than an error threshold value, and adding the target angular velocity into an abnormal angular velocity set if the difference value is larger than or equal to the error threshold value, so that N=N+1;
S216, repeating the step S215, and traversing all angular velocities in the angular velocity sequence to obtain a traversed abnormal angular velocity set N;
s217 determines whether N is greater than 0:
If N >0, then step S218 is performed;
If n=0, step S219 is executed;
s218, eliminating abnormal angular velocity from the angular velocity sequence, updating the angular velocity sequence, and returning to the step S214 to recalculate the reference angular velocity;
S219, eliminating repeated records in the angular velocity sequence to obtain a final angular velocity sequence and a reference angular velocity, performing overlapping comparison according to the equipment number of the final angular velocity sequence and the equipment number of the equipment included angle data set, taking the monitoring equipment corresponding to the overlapped equipment number as a monitoring equipment set with accurate clock operation, and taking the rest monitoring equipment as a first equipment set to be calibrated.
In this embodiment, step S22 includes:
S221, selecting a current first device to be calibrated from the first device to be calibrated set, and acquiring a corresponding first included angle value;
S222, selecting a value with the largest included angle from a monitoring equipment set with accurate clock operation as a second included angle value, and calculating the undetermined angular velocity according to an angular velocity calculation formula;
s223 judges the difference between the pending angular velocity and the reference angular velocity:
If the difference value is larger than the error threshold value, judging the current first equipment to be calibrated as problem equipment, and classifying the problem equipment into a second equipment set to be calibrated;
if the difference value is smaller than or equal to the error threshold value, judging that the current first equipment to be calibrated is normal equipment, and classifying the first equipment to be calibrated into a normal equipment set;
S224, repeating the steps S221-S223, traversing all first equipment to be calibrated in the first equipment to be calibrated to obtain a second equipment set to be calibrated and a normal equipment set, and calculating constant angular velocity according to the normal equipment set.
Referring to fig. 4, a specific example is described as follows:
step 1, sorting the included angles from small to large: and (3) sorting the data obtained in the step one according to the included angle, wherein after sorting, each element further comprises the included angle, the equipment number and the time stamp. After completion, step 2 is entered.
Step 2, constructing a cyclic list: and (3) constructing a cyclic list for the ordered set in the step (1), wherein each element points to the next record which is immediately adjacent after the ordering. The last record points to the first record, forming a single circular list of data sets. After completion, step 3 is entered.
Step 3, calculating the angular velocity between the nodes: the angular velocity is calculated by using the angular velocity formula through the cyclic list. In the cyclic list, the current element is selected, then the element that is a suitable fixed number (e.g., interval M/2-1, where M is the total number of cyclic list elements) is selected, then the angular velocity is calculated, and the starting device number and the ending device number are recorded. If there is a sequence exchange of the starting device number and the ending device number in 2 records, the fixed number to be selected is adjusted to be the original fixed number-1, and then the angular velocity is recalculated. After completion, step 4 is entered.
Step 4, establishing an angular velocity sequence: generating an angular velocity sequence through the step 3, wherein each sequence comprises 3 pieces of information: angular velocity, start device number, and end device number. After completion, step 5 is entered.
Setting n=0: this variable is set to record the number of abnormal angular velocities in the angular velocity sequence set. After completion, step 6 is entered.
Step 6 calculates the average angular velocity w 1: and calculating the average angular velocity in the angular velocity set, and entering step 7 after the completion of the calculation.
Step 7, selecting an angular velocity w: the next angular velocity not traversed is selected from the set of angular velocities, and if traversed for the first time, the first angular velocity is selected. After completion, the process proceeds to step 8.
Steps 8|w-w 1|<λ*w1: it is determined whether the difference between the selected w and the average angular velocity of the set is less than λ×w 1, where λ is the error of the measured angle and is typically 0.05. If the video is a high-definition video, the measured included angle is more accurate, and lambda can be set to be 0.01. If the difference between the selected w and the average value w 1 is small, the recorded angular velocity is accurate, and the step 9 is entered, otherwise, the angular velocity is inaccurate, and the step 10 is entered.
Step 9 is traversed: and judging whether all elements in the angular velocity sequence are traversed, if so, entering a step 12, otherwise, not traversing the angular velocity, and entering a step 7.
Step 10n=n+1: and counting abnormal angular velocity data, increasing by 1 once abnormal values are found, and entering step 11 after the abnormal values are completed.
Step 11, collecting problematic angular velocities: the record is classified as a record of the angular velocity in question for subsequent processing. After completion, step 9 is entered.
Step 12n >0: judging whether the angular velocity set has abnormal angular velocity, if so, proceeding to step 13, otherwise proceeding to step 14.
Step 13, eliminating abnormal angular velocity: and (3) eliminating the abnormal angular velocity from the original angular velocity set to form a new angular velocity sequence, reducing the number of abnormal angular velocity elements, reducing the influence of the abnormal angular velocity on the average angular velocity, enabling the average angular velocity of the area to be more accurate, facilitating the elimination of the abnormal clock equipment, and entering the step (4) after the completion of the step.
Step 14, generating a clock accurate device set and a suspected problematic device set: in the rest angular velocity sequence, all the initial equipment numbers and the termination equipment numbers are accurate in clock, and repeated records are removed. The device angle dataset is divided into 2 sets in total according to the device number. And (3) carrying out overlapping comparison on the equipment numbers of the final angular velocity sequence and the equipment numbers of the equipment included angle dataset, taking the monitoring equipment corresponding to the overlapped equipment numbers as a monitoring equipment set with accurate clock operation, taking the rest monitoring equipment as a first equipment set to be calibrated, and finally calculating that w 1 is the constant angular velocity w 0 of the area. After completion step 15 is entered.
Step 15 selects a first device to be calibrated: angular velocity anomalies are typically caused by clock anomalies of the originating device or the terminating device. Further judgment of the first device to be calibrated is needed, so that one record is selected from the first device to be calibrated, the first record is selected if the first record is selected, and the next record which is not judged is selected if the first record is not selected. After completion step 16 is entered.
Step 16, selecting the exact device with the largest difference to the included angle to calculate w: and selecting the element of the accurate equipment with the largest included angle difference value from the accurate equipment set, and then calculating w by using an angular velocity formula. After completion, step 17 is entered.
Step 17|w-w 1|<λ*w1: and as in step 8, judging the difference between w and w 1, if the difference is large, determining that the equipment is problematic equipment, and entering step 19, otherwise, determining that the equipment is normal equipment, and entering step 18.
Step 18, the equipment is gathered into normal equipment: and judging the first device to be calibrated as a normal device. After completion step 20 is entered.
Step 19 this equipment is grouped into problem equipment: the devices are grouped into a second set of devices to be calibrated and after completion step 20 is entered.
Step 20 traverses whether the first device to be calibrated: and judging whether all the first devices to be calibrated are traversed, if so, entering a step 21, otherwise, entering a step 15, and continuing to traverse the next first device to be calibrated.
Step 21 ends: all the procedures are described as completed, the normal set of devices and the second set of devices to be calibrated have been determined, and the constant angular velocity w 0 of this area is calculated.
Calculating the deviation time
In step two, a constant angular velocity w 0 has been obtained by certain data processing and calculation, and the relevant data of the normal device and the second device to be calibrated are distinguished. For all second devices to be calibrated, a formula is used to calculate the time difference Δt. This Δt refers to the calibration time offset that the second device to be calibrated needs to perform in generating the video, and this step calculates all the second device time offset sets to be calibrated, each element including the device ID and the time offset.
Specifically, in an embodiment of the present invention, step S3 includes:
S31, selecting a current second device to be calibrated from the second device set to be calibrated;
s32, initializing two variables theta and ID, wherein theta is set to be pi, and the theta is used for recording the maximum included angle difference value; an ID set to 0 for recording the device number of the normal device;
S33, selecting a current normal device from the normal device set, and calculating an included angle difference value between an included angle of the current normal device and an included angle of the current second device to be calibrated;
S34, comparing the included angle difference value with a variable theta:
If the included angle difference value > theta, updating the variable theta to be the included angle difference value, and recording the ID of the current normal equipment;
If the included angle difference value is less than or equal to theta, returning to the step S33 to reselect a current normal device;
S35, repeating the steps S33-S34, traversing all normal devices in the normal device set, and obtaining finally updated theta and ID;
s36, calculating the time deviation delta T of the current second equipment to be calibrated according to the finally updated theta, and recording the equipment number and the time deviation delta T of the current second equipment to be calibrated into a time deviation set;
S37, repeating the steps S31-S36, traversing all the second devices to be calibrated in the second device set to be calibrated, and outputting to obtain a final time deviation set, wherein each record contains a device number and a corresponding time deviation delta T in the time deviation set.
The calculation formula of the time deviation is as follows:
Wherein Δt is a time deviation, w 0 is a constant angular velocity, T 2 is a time stamp of a normal device, T 1 is a time stamp of a second device to be calibrated, θ is an included angle difference value, θ=a 1-a2,a1 is an included angle of the second device to be calibrated, and a 2 is an included angle of the normal device.
Referring to fig. 5, a specific example is described as follows:
Step 1, selecting a second device to be calibrated: and selecting one device from the second device set to be calibrated for inspection. If this is the first round of the procedure, selecting a first second device to be calibrated in the set; if one or more previous rounds of inspection have been performed, a second device to be calibrated is selected that has not been inspected. After completion, step 2 is entered.
Step 2, initializing variables θ and ID: two variables are initialized: θ is set to be-pi for recording the maximum angle difference; the ID is set to 0 for recording the device number of the normal device. These two variables will be updated in a subsequent step. After completion, step 3 is entered.
Step 3, selecting a normal device: and selecting one normal device from the normal device set for comparison. If the step is performed for the first time, selecting a first normal device; if at least one round of alignment has been completed, the next normal device that has not been aligned is selected. After the selection is completed, the process proceeds to step 4.
Step 4, calculating an included angle difference value: and calculating the difference value of the included angle between the selected normal equipment and the current second equipment to be calibrated. This difference will be used for subsequent comparison and evaluation and after completion step 5 is entered.
Step 5 included angle difference > θ: the currently calculated angle difference is compared with the variable θ. If the difference is greater than θ, updating θ to the greater included angle difference, and recording the ID of the corresponding normal device, because this indicates that a normal device having a greater angular deviation from the second device to be calibrated is found, step 6 is required, otherwise step 7 is required.
Step 6, updating theta and ID: and after confirming that the theta and the ID need to be updated, setting the theta as a new included angle difference value, and updating the ID as the ID of the current normal equipment. This ensures that the maximum angle difference and corresponding normal device information is always recorded. After completion, step 7 is entered.
Step 7, traversing whether the equipment is normal: and confirming whether all normal devices have been compared with the second device to be calibrated for the difference of the included angles. If yes, the θ recorded at present is the maximum included angle difference value, and the corresponding ID is the ID of the normal device corresponding to the maximum included angle difference value, and then step 8 is entered; if not, the step 3 is required to be returned to continue traversing the rest of the normal devices.
Step 8, calculating delta T: and calculating the time deviation delta T of the second equipment to be calibrated relative to the normal equipment corresponding to the recorded maximum included angle difference value by using a calculation formula of the time deviation. This time offset may be positive or negative. Subsequently, the ID of the second device to be calibrated and the calculated Δt are recorded into a time offset set. After completion, step 9 is entered.
Step 9, traversing all second devices to be calibrated: and judging whether the time deviations of all the second equipment to be calibrated are calculated and recorded. If yes, go to step 10; otherwise, enter step 1.
Step 10 forms a set of time offsets for the second device to be calibrated: a complete set of time offsets is obtained. In this set, each record contains a device ID and the corresponding time offset Δt for that device.
Four pair device time calibration
According to the third step a time-aligned data set of the problem device is formed. For all devices in the dataset that have the same ID, the video data they generate will be time-aligned. The specific calibration period should be traced back from the current set date (including the current date) to the date of the last calibration (excluding the last calibration date). If the device has not been calibrated, then the calibration time is by the selected date all video data events are calibrated.
Specifically, in an embodiment of the present invention, step S4 includes:
S41, selecting one piece of current time calibration data from the time deviation set, and searching all monitoring videos corresponding to equipment according to equipment numbers in the current time calibration data to serve as videos to be calibrated;
S42, performing time calibration on the video to be calibrated according to the time deviation delta T in the current time calibration data, and adjusting the time stamp of the video to be calibrated to obtain a calibrated video;
s43, repeating the steps S41-S42, traversing all time calibration data in the time deviation set, and completing time calibration for all videos to be calibrated.
Referring to fig. 6, a specific example is described as follows:
Step 1, selecting time calibration data: and selecting one piece of data from the time calibration data set for processing. If the operation is the primary operation, selecting a first item in the data set; if processing has been previously performed, the next piece of unprocessed data is selected. After the selection is completed, step 2 is entered.
Step 2, obtaining video data: all video data generated by the device is retrieved based on the device number in the selected time alignment data. These data should cover a range from the last calibration date (from the earliest date available if first calibrated) to the current selected date, including the day of the selected date. After the completion of the search, step 3 is performed.
Step 3 video time calibration: and carrying out time calibration on the retrieved video data by using delta T, and ensuring that the time stamps of all videos are adjusted according to a calibration formula. After the calibration is completed, step 4 is continued.
Step 4, calibration data traversal checking: it is checked whether all devices identified as second devices to be calibrated have completed time calibration. If yes, go to step 5; if not, returning to the step 1, selecting the next unprocessed time calibration data to proceed.
And 5, completing calibration: confirm that all video times generated by the second device to be calibrated have been calibrated until the selected date, and end the entire calibration procedure.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A method of calibrating video time in a data center, comprising the steps of:
S1, acquiring a monitoring video in monitoring equipment, and calculating the included angle between the sun and the ground under each monitoring equipment by using an included angle calculation method according to the monitoring video to form an equipment included angle data set, wherein the equipment included angle data set comprises the included angle under the monitoring equipment, a timestamp of the monitoring equipment and an equipment number;
S2, calculating the angular velocity in the equipment included angle data set according to a calculation formula of the angular velocity, screening according to the angular velocity to obtain a first equipment set to be calibrated, and screening the first equipment to be calibrated by using a screening algorithm to obtain a second equipment set to be calibrated;
s3, traversing a second equipment set to be calibrated, and calculating to obtain a time deviation set of the second equipment set to be calibrated;
and S4, performing time calibration on the second equipment set to be calibrated according to the time deviation set to obtain the calibrated monitoring equipment.
2. A method of time alignment of video in a data center as claimed in claim 1, wherein step S1 comprises:
S11, acquiring historical weather data, screening out the date of sunny weather from the historical weather data, acquiring corresponding monitoring videos according to the date of sunny weather, filtering the monitoring videos by adopting a processing algorithm, only reserving daytime videos, and acquiring monitoring equipment to be detected according to the source of the daytime videos, wherein each monitoring equipment to be detected comprises equipment numbers;
S12, selecting a monitoring device to be detected as a target device, and arranging daytime videos in the target device according to a time sequence to form a video sequence;
s13, selecting a daytime video from the video sequence as a target video;
S14, selecting one image frame in the target video as a target image frame;
S15, identifying a target image frame by using a machine vision method, if the shadow and the timestamp of the image frame are identified, entering a step S16, otherwise, returning to the step S14 to reselect one image frame as the target image frame;
S16, measuring the height of a person or an object and the length of a shadow by using a machine vision algorithm, expressing the measured height and length by pixel values, substituting the pixel values of the height and the length into an included angle calculation formula for calculation, and obtaining the included angle between the sun and the ground;
S17, judging whether the included angle is in a threshold range, if so, correlating the included angle, the timestamp of the target image frame and the equipment number, outputting the correlated included angle, the timestamp and the equipment number as equipment included angle data of target equipment, returning to the step S12, selecting another monitoring equipment as the target equipment, and if not, removing the image frame from the target video and returning to the step S14;
s108, repeating the steps S12-S17 until all the monitoring devices to be detected are selected, and outputting to obtain a device included angle data set.
3. A method of calibrating video time in a data center as claimed in claim 2, wherein the angle calculation formula is:
Where h is the pixel value of the height; l is the pixel value of the length; b is the included angle between the monitoring interface and the horizontal plane in the target image frame; determining an included angle according to the direction of the shadow relative to the person or object:
When the shadow is positioned on the west side of a person or an object, the included angle is a;
when the shadow is positioned at the east side of a person or an object, the included angle is
4. A method of calibrating video time in a data center as claimed in claim 3, wherein in step S17, the threshold range is:
when the included angle is a, the threshold range is [30 degrees, 80 degrees ];
When the included angle is The threshold range is 110, 150.
5. A method of time alignment of video in a data center as claimed in claim 1, wherein step S2 comprises:
S21, taking a timestamp in the equipment included angle data set as a measurement point of each equipment included angle data, determining the angular velocity between measurement points by utilizing an angular velocity calculation formula according to the uniform change rule of the equipment included angle data set based on the included angle, acquiring a reference angular velocity, and identifying a monitoring equipment set with accurate clock running according to the difference between the calculated value of the angular velocity and the reference angular velocity, wherein monitoring equipment not in the set is used as a first equipment set to be calibrated;
S22, taking an included angle of each first device to be calibrated in the first device set to be calibrated as a first included angle value, selecting a value with the largest included angle from a monitoring device set with accurate clock operation as a second included angle value, taking a monitoring device corresponding to the second included angle value as a second device, taking the second device as a reference, calculating a to-be-calibrated angular velocity between each first device to be calibrated and the second device according to the first included angle value, the second included angle value, a timestamp of the first device to be calibrated and a timestamp of the second device, judging whether the clock of the first device to be calibrated is accurate or not according to the difference between the to-be-calibrated angular velocity and the reference angular velocity, and taking the first device to be calibrated with a clock problem as the second device set to be calibrated.
6. The method for calibrating video time in a data center according to claim 5, wherein step S21 comprises:
S211, sorting the equipment included angle data sets according to the included angles from small to large to obtain a first sequence table, wherein each element in the first sequence table comprises an included angle, a time stamp and an equipment number; unidirectional directional links are carried out on the elements in the first sequence table according to the adjacent sequence, each element points to the next adjacent element, and the last element points to the first element, so that a cyclic list is constructed;
S212, setting element interval numbers through a cyclic list, and calculating the angular velocity among elements by using an angular velocity formula according to the element interval numbers; generating an angular velocity sequence according to the angular velocity calculated by the cyclic list, wherein one record in the angular velocity sequence comprises the angular velocity and the associated initial equipment number and the associated termination equipment number;
S213 sets an abnormal angular velocity set N, initially n=0;
S214, calculating the average value of all angular velocities in the angular velocity sequence, and taking the average value as a reference angular velocity;
S215, selecting one angular velocity from the angular velocity sequence as a target angular velocity, calculating a difference value between the target angular velocity and a reference angular velocity, judging that the target angular velocity is accurate if the difference value is smaller than an error threshold value, and adding the target angular velocity into an abnormal angular velocity set if the difference value is larger than or equal to the error threshold value, so that N=N+1;
S216, repeating the step S215, and traversing all angular velocities in the angular velocity sequence to obtain a traversed abnormal angular velocity set N;
s217 determines whether N is greater than 0:
If N >0, then step S218 is performed;
If n=0, step S219 is executed;
s218, eliminating abnormal angular velocity from the angular velocity sequence, updating the angular velocity sequence, and returning to the step S214 to recalculate the reference angular velocity;
S219, eliminating repeated records in the angular velocity sequence to obtain a final angular velocity sequence and a reference angular velocity, performing overlapping comparison according to the equipment number of the final angular velocity sequence and the equipment number of the equipment included angle data set, taking the monitoring equipment corresponding to the overlapped equipment number as a monitoring equipment set with accurate clock operation, and taking the rest monitoring equipment as a first equipment set to be calibrated.
7. The method for calibrating video time in a data center as claimed in claim 6, wherein step S22 comprises:
S221, selecting a current first device to be calibrated from the first device to be calibrated set, and acquiring a corresponding first included angle value;
S222, selecting a value with the largest included angle from a monitoring equipment set with accurate clock operation as a second included angle value, and calculating the undetermined angular velocity according to an angular velocity calculation formula;
s223 judges the difference between the pending angular velocity and the reference angular velocity:
If the difference value is larger than the error threshold value, judging the current first equipment to be calibrated as problem equipment, and classifying the problem equipment into a second equipment set to be calibrated;
if the difference value is smaller than or equal to the error threshold value, judging that the current first equipment to be calibrated is normal equipment, and classifying the first equipment to be calibrated into a normal equipment set;
S224, repeating the steps S221-S223, traversing all first equipment to be calibrated in the first equipment to be calibrated to obtain a second equipment set to be calibrated and a normal equipment set, and calculating constant angular velocity according to the normal equipment set.
8. A method of time alignment of video in a data center as claimed in claim 7, wherein step S3 comprises:
S31, selecting a current second device to be calibrated from the second device set to be calibrated;
s32, initializing two variables theta and ID, wherein theta is set to be pi, and the theta is used for recording the maximum included angle difference value; an ID set to 0 for recording the device number of the normal device;
S33, selecting a current normal device from the normal device set, and calculating an included angle difference value between an included angle of the current normal device and an included angle of the current second device to be calibrated;
S34, comparing the included angle difference value with a variable theta:
If the included angle difference value > theta, updating the variable theta to be the included angle difference value, and recording the ID of the current normal equipment;
If the included angle difference value is less than or equal to theta, returning to the step S33 to reselect a current normal device;
S35, repeating the steps S33-S34, traversing all normal devices in the normal device set, and obtaining finally updated theta and ID;
s36, calculating the time deviation delta T of the current second equipment to be calibrated according to the finally updated theta, and recording the equipment number and the time deviation delta T of the current second equipment to be calibrated into a time deviation set;
S37, repeating the steps S31-S36, traversing all the second devices to be calibrated in the second device set to be calibrated, and outputting to obtain a final time deviation set, wherein each record contains a device number and a corresponding time deviation delta T in the time deviation set.
9. The method for time alignment of video in a data center as claimed in claim 8, wherein the time offset is calculated by the formula:
Wherein Δt is a time deviation, w 0 is a constant angular velocity, T 2 is a time stamp of a normal device, T 1 is a time stamp of a second device to be calibrated, θ is an included angle difference value, θ=a 1-a2,a1 is an included angle of the second device to be calibrated, and a 2 is an included angle of the normal device.
10. A method of time alignment of video in a data center as claimed in claim 8, wherein step S4 comprises:
S41, selecting one piece of current time calibration data from the time deviation set, and searching all monitoring videos corresponding to equipment according to equipment numbers in the current time calibration data to serve as videos to be calibrated;
S42, performing time calibration on the video to be calibrated according to the time deviation delta T in the current time calibration data, and adjusting the time stamp of the video to be calibrated to obtain a calibrated video;
s43, repeating the steps S41-S42, traversing all time calibration data in the time deviation set, and completing time calibration for all videos to be calibrated.
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