WO2019008635A1 - Procédé de réglage d'un dispositif de surveillance vidéo et dispositif de surveillance vidéo - Google Patents

Procédé de réglage d'un dispositif de surveillance vidéo et dispositif de surveillance vidéo Download PDF

Info

Publication number
WO2019008635A1
WO2019008635A1 PCT/JP2017/024350 JP2017024350W WO2019008635A1 WO 2019008635 A1 WO2019008635 A1 WO 2019008635A1 JP 2017024350 W JP2017024350 W JP 2017024350W WO 2019008635 A1 WO2019008635 A1 WO 2019008635A1
Authority
WO
WIPO (PCT)
Prior art keywords
video
threshold
error
alarm
monitoring
Prior art date
Application number
PCT/JP2017/024350
Other languages
English (en)
Japanese (ja)
Inventor
浜田高宏
Original Assignee
株式会社Ｋ－Ｗｉｌｌ
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 株式会社Ｋ－Ｗｉｌｌ filed Critical 株式会社Ｋ－Ｗｉｌｌ
Priority to PCT/JP2017/024350 priority Critical patent/WO2019008635A1/fr
Priority to JP2019528202A priority patent/JP7033797B2/ja
Priority to CN201780089397.8A priority patent/CN110870305B/zh
Publication of WO2019008635A1 publication Critical patent/WO2019008635A1/fr

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N17/00—Diagnosis, testing or measuring for television systems or their details

Definitions

  • the present invention relates to an adjustment method and an image monitoring apparatus of an image monitoring apparatus capable of mechanically detecting an error of an image and an audio included in a digital image signal.
  • video distribution services such as Internet content distribution services and Internet Protocol TeleVision (IPTV) business are expected to expand in the future, and will be distributed accordingly. Since the number of such content also increases dramatically, it is considered to be practically difficult for the observer to monitor all the content. Therefore, it can be said that improving the accuracy of error detection in machine monitoring to human level is a pressing issue in all video distribution services.
  • IPTV Internet Protocol TeleVision
  • Patent Document 1 discloses an audio inspection method for detecting an audio error caused by a video error caused by noise caused by various causes in a digital video signal and a noise generated by a different cause in a digital audio signal. It is done.
  • the error detection is performed by comparing the characteristic value (parameter) of the audio signal with the threshold, but there is a problem as to how to set the threshold. That is, if the threshold is set to be strict, error detection is frequently performed, but if many of the detected errors are negligible for the viewer, detection may be wasted. On the other hand, if the threshold value is set loosely, although the frequency of error detection decreases, there is a risk that the viewer may miss an error that can not be ignored. This is one of the reasons why machine surveillance is inferior to human surveillance. Since changing one parameter affects other parameters, especially when there are multiple parameters that are related to each other, two or more parameters must be changed at the same time, and appropriate threshold settings should be made. Is very difficult.
  • One of the objects of the present invention is to provide a method of adjusting an image monitoring apparatus for appropriately detecting an image and / or an error generated depending on content in a digital image signal.
  • Another object of the present invention is to provide an image monitoring apparatus for detecting an image and / or an error properly through learning in a digital image signal.
  • a method of adjusting an image monitoring apparatus wherein an image signal corresponding to an image to be monitored and / or audio is input to detect that an error occurs in a monitoring item.
  • the video monitoring apparatus is configured to determine that the error has occurred when all of a plurality of parameters that change according to the video signal exceed a threshold determined according to the monitoring item.
  • At least two of the plurality of parameters are mutually related
  • the threshold values of the plurality of parameters are respectively determined to create a plurality of threshold value groups, which are stored in a memory, It is characterized in that any set of the threshold group stored in the memory is selected according to contents of video and / or audio to be monitored.
  • the inventor of the present invention has determined that the appropriate value of the threshold group is the content of the video and / or audio content to be monitored ("movie”, “drama”, “variety”, “sports”, “documentary” , “Entertainment”, “animation”, etc.) have been found to be unevenly distributed.
  • the present invention has been derived based on such findings. That is, if the threshold values of the plurality of parameters are respectively determined and a plurality of sets of threshold values are created and stored in the memory, they are stored in the memory according to the content of video and / or audio to be monitored. By selecting any set of the threshold group, a threshold group more appropriate for monitoring can be set as compared to the case where a single threshold group is used, so erroneous detection of an error in the image monitoring apparatus is effectively performed. It can be prevented.
  • the video monitoring device performs learning in parallel with the video and / or audio monitoring operation, updates at least one threshold of the threshold group based on the learning result, and stores the updated threshold group in a memory It is preferable to store in
  • the video surveillance device issues an alarm when an error is detected.
  • a video signal corresponding to the video and / or audio to be monitored is input, and the alarm is issued at predetermined time intervals, the first event of the alarm for the error to be issued Count the number of events and the number of second events for which the alarm has been issued for errors that should not be issued, Furthermore, a video signal for inspection whose content and place of occurrence of the error are known is input to the video monitoring apparatus, and the number of third events for which the alarm has not been issued for an error that should be issued.
  • Count An evaluation value is determined by weighting and calculating the number of obtained first events, the number of second events, and the number of third events, and the threshold group is determined based on the determined evaluation values. It is preferable to update the
  • the learning it is preferable to count the number of the first events, the number of the second events, and the number of the third events while individually changing the thresholds of the threshold group.
  • a video monitoring apparatus is a video monitoring apparatus that receives a video signal corresponding to video and / or audio to be monitored and detects that an error has occurred in a monitoring item.
  • a threshold storage unit that stores a plurality of parameters that change according to the video signal, wherein at least two of the plurality of parameters are associated with each other;
  • a determination unit that determines that the error has occurred and issues an alarm when all of the plurality of parameters exceed the threshold determined according to the monitoring item;
  • a learning unit that performs learning in parallel with the video and / or audio monitoring operation;
  • An updating unit that updates the threshold based on a learning result in the learning unit;
  • a video storage unit for storing video signals before and after the occurrence of the error when the determination unit detects the occurrence of the error; The learning unit may learn by analyzing the notification result of the error.
  • the result of the error notification is analyzed and classified into, for example, "correct alarm”, “unwanted alarm”, “pass through”, etc., whereby the learning unit performs learning to make the threshold more appropriate.
  • the error detection accuracy is improved because the value can be updated to
  • the learning unit inputs a video signal corresponding to the video and / or audio to be monitored, the alarm is issued for an error that should be issued at a constant time interval. Counts the number of first events and the number of second events for which an alarm should have been issued for errors that should not have triggered an alarm When the signal is input, the number of third events for which the alarm has not been issued for the error that should be issued is counted, and the number of the first event obtained and the number of the second events It is preferable to calculate an evaluation value by calculating by weighting the number of cases and the number of cases of the third event, and ranking the threshold values stored in the threshold storage unit based on the obtained evaluation values.
  • the learning unit count the number of first events, the number of second events, and the number of third events while individually changing the threshold value of the monitoring item.
  • the learning unit may be configured to cause the alarm to be issued for an error that should be issued for the m-th video signal of the M video signals during the time t1 to t2.
  • T (m) be the number of events
  • F (m) be the number of second events for which an alarm should have been issued for errors that should not have triggered an alarm.
  • H (m) the number of third events for which the alarm has not been issued.
  • Wt (m) is the weight of the first event
  • Wf (m) is the weight of the second event
  • Wh (m) is the weight of the third event
  • the video storage unit cuts out and stores the input video signal with a predetermined length at a predetermined timing.
  • a display unit that displays the learning result of the learning unit and reproduces video and / or audio based on the video signal before and after the occurrence of the error.
  • the updating unit prioritizes and lists new threshold candidates that replace the current threshold.
  • voice includes sounds in addition to human and animal voices.
  • Video signal includes the case of either video signal or audio signal.
  • Content is the content of the video. Specifically, there are “movie”, “drama”, “variety”, “sports”, “documentary”, “entertainment”, “animation” etc. as the major classification of content. On the other hand, sub-classification of contents, if taking “sports” as an example, includes “baseball”, “soccer”, “volleyball”, “judo”, “sumo”, “land athletics” and the like.
  • “Learning” associates the monitoring result of watching the video and / or audio flowing in real time with the monitoring result of the same video and / or audio by the video monitoring device, and uses it as a threshold Including feedback.
  • “Interrelated” means that adjusting one parameter affects the other parameter.
  • the “above the threshold” includes both cases where the threshold is exceeded and below the threshold.
  • the present invention it is possible to provide a method of adjusting an image monitoring apparatus for appropriately detecting an image and / or an error generated according to content in a digital image signal. Also, according to the present invention, it is possible to provide an image monitoring apparatus which detects an image and / or an error properly through learning in a digital image signal.
  • FIG. 2 is a block diagram showing an image monitoring apparatus 100.
  • FIG. 6 is a diagram showing an example of inspection items in the video / audio monitoring unit 103 displayed on the display unit 107.
  • FIG. 6 is a diagram showing an example of alarm information displayed on a display unit 107. It is a figure which shows the example of the parameter for every monitoring item displayed on the display part 107.
  • FIG. It is a figure which shows the example which put together the correct item alarm which generate
  • FIG. 1 is a block diagram showing an image monitoring apparatus 100.
  • a video monitoring apparatus 100 inputs a main input unit 101 for inputting a video signal (hereinafter referred to as a video signal to be inspected) corresponding to video and / or audio to be monitored, and a video signal for inspection.
  • a video signal to be inspected a video signal corresponding to video and / or audio to be monitored
  • Sub-input unit 102 video / audio monitoring unit (determination unit) 103 incorporating memory for storing threshold, video / audio clip storage unit (video storage unit) 104, alarm output unit 105, internal memory (threshold A parameter optimization learning unit (learning unit and updating unit) 106 including a storage unit 106a, a display unit 107 such as a display on which the information displayed by the supervisor MN can be viewed, and the supervisor MN can input information And a supervisor input unit 108 such as a keyboard.
  • FIG. 2 is a diagram showing an example of inspection items in the video / audio monitoring unit 103 displayed on the display unit 107.
  • the video signals to be monitored are video signals of all formats such as an SDI signal, a file, an IP format, and HDMI (registered trademark).
  • the supervisor MN changes the inside of the box corresponding to each monitoring item via the supervisor input unit 108 while looking at the monitoring item displayed on the display unit 107, and performs “inspection” or “off” for each monitoring item. You can choose one of these.
  • the inspection item for which the supervisor has selected “inspection” is to be monitored by the video monitoring apparatus 100, but the inspection item for which “off” is selected is not monitored.
  • monitoring items related to the image include “freeze”, “black out”, “block freeze”, “block black out”, “block noise”, “red blink”, “brightness blink”, “scene change”, “image reverse” There are “line noise”, “cut point abnormality”, and “time code discontinuous”.
  • monitoring items relating to audio include “mute”, “cut-off mute”, “audio pop noise”, “sound skipping”, “voice noise”, “loudness” and “true peak”. The inspection items are not limited to these.
  • a plurality of thresholds (a threshold group, the details will be described later) are set for each monitoring item.
  • a threshold group the details will be described later.
  • the parameter is transmitted to the voice monitoring unit 103, where parameters corresponding to various sensitivity adjustments and durations that can be applied to the monitoring algorithm are calculated.
  • the video / voice monitoring unit 103 compares a plurality of obtained parameters with a threshold value corresponding to the monitoring item, and when all the parameters exceed the threshold value, an error corresponding to the monitoring item is detected. It judges and outputs the alarm signal matched with the said monitoring item to the alarm output part 105.
  • the alarm output unit 105 inputs alarm information including the error occurrence time, the content of the detected error, and the severity of the error to the display unit 107 according to the input alarm signal, the display unit 107 receives the alarm information. It can be displayed and viewed by the observer MN. Also, the video / audio monitoring unit 103 cuts out video signals before and after the parameter exceeds the threshold and stores the video signals in the video / audio clip storage unit 104.
  • FIG. 3 is a diagram showing an example of alarm information displayed on the display unit 107.
  • the lower part of the screen shows the detected error and content in chronological order
  • the upper part of the screen shows "category” indicating that the error is video or audio, and the level of error is normal or severe.
  • the "class” indicating, the "inspection item” indicating the type of error, and the "number of occurrences of error” are displayed together.
  • the supervisor MN selects any error via the supervisor input unit 108
  • the video / audio clip storage unit 104 reads out the corresponding video signal before and after the error, and the display unit 107 It is input. Since the video and / or the sound before and after the error are output from the display unit 107 by this, the supervisor MN can actually visually recognize or listen to the content of the error.
  • the correct answer (correct answer If an alarm is issued for an error that should not issue an alarm (an unnecessary alarm) or if an alarm is not issued for an error that should issue an alarm Can be recognized respectively.
  • it is ideal that all alarms issued when the image monitoring apparatus 100 detects an error are all correct alarms, but in reality, unnecessary alarms and passing through occur. This results from the fact that the error detection standard by machine monitoring of the video / voice monitoring unit 103 does not exactly match the error detection standard by monitoring of the supervisor MN. Therefore, unless the error detection by the machine monitoring approaches the error detection by the human monitoring, the monitoring by the image monitoring apparatus 100 alone becomes difficult.
  • the threshold value of the parameter set for each monitoring item is changed. This is called threshold tuning.
  • the number of parameters is I (m)
  • the number of threshold values to be tuned is 1 ⁇ I (1) + 2 ⁇ I (2) +. It can be seen that I (m) increases as the number of monitoring items increases.
  • FIG. 4 is a diagram showing an example of parameters for each monitoring item displayed on the display unit 107.
  • the supervisor MN can now input an arbitrary numerical value for each parameter in the box corresponding to the parameter via the supervisor input unit 108 while looking at the parameter displayed on the display unit 107. There is.
  • sensitivity threshold (activity) For example, in response to the inspection item "freeze”, 4 parameters "sensitivity threshold (activity)”, “sensitivity threshold (noise)”, “time threshold (start)”, and “time threshold (end)” There is one.
  • Graph scale represents the scale of the graph for display, and is not a parameter here. That is, in order to detect a video freeze as an error, thresholds (threshold groups) of four parameters must be set appropriately.
  • sensitivity threshold (activity)” and “sensitivity threshold (noise)” are upper and lower limit values of the variance for each small block included in one video frame as a parameter, and they are mutually related. It can be said that they fit. Such parameters are disclosed in detail in WO 2015-059782.
  • time threshold (start) and “time threshold (end)” indicate the length of a period during which it is determined that the video is phrased, and they are mutually related. Therefore, when one of the thresholds is adjusted, it is impossible to appropriately detect the freeze unless the other threshold is also changed.
  • an appropriate threshold value is not input for all inspection items, unnecessary alarms or omissions at the time of error detection are caused.
  • a default threshold initial value
  • Such a default threshold can be stored and used, for example, in the built-in memory of the video / audio monitoring unit 103.
  • the unnecessary alarm and the passing through are reduced for one content, but the unnecessary alarm and the passing occurs for another content. I understand.
  • FIG. 5 is a diagram showing an example in which the correct answer alarm, the unnecessary alarm, and the slip-through that occur when the error detection is performed using the same threshold are summarized for each monitoring item and content. From the example of FIG. 5, it can be seen that the occurrence frequency of the correct alarm, the unnecessary alarm, and the slip-in differs for each content. Based on the examination result, the inventor has found that it is sufficient to determine the default threshold according to the content of the content. Such default thresholds can be determined from simulations and accumulated experience.
  • the default threshold is determined according to the content of the content, there is a possibility of reducing unnecessary alarms and passing through to some extent, but it is not always optimal. Even if the contents of the content are the same, the frequency of unnecessary alarms and passing through may change depending on the reception state and the like. Therefore, in parallel with the monitoring operation, by causing the video monitoring apparatus 100 to learn about the currently input video signal, it is determined whether the default threshold is appropriate or not, and it is preferable to further update it if it is not appropriate. It can be said. This makes it possible to increase the number of correct alarms and reduce the frequency of unnecessary alarms and passing through.
  • the updated threshold may be newly set as the default threshold of the content.
  • (Learning mode) The learning function of the video surveillance device 100 will be described below. In the following learning example, it is possible to determine which is better for the case where there are threshold candidates to be changed with respect to the default threshold.
  • the supervisor MN sets a predetermined learning period from the supervisor input unit 108. Then, the video surveillance device 100 can perform learning during this learning period.
  • the monitoring operation is performed using the default threshold value for one monitoring item in M video signals of the same type of content.
  • an error detected by the image monitoring apparatus 100 is displayed on the display unit 107 as shown in FIG. 3 during a learning period (time t1 to t2, for example, a time unit such as hours, days, weeks, or months). . Since the errors displayed as shown in FIG. 3 may include inappropriate ones, it is necessary to check whether the errors are appropriate for learning.
  • the supervisor MN reads the video signals before and after the error stored in the video / audio clip storage unit 104 by designating one of the errors displayed in FIG. Video and / or audio can be viewed on the display unit 107. As a result, the supervisor MN can count the number of correct alarms generated during the time t1 to t2 and the number of unnecessary alarms. Since the video signal before and after each error stored in the video / audio clip storage unit 104 has a length of about 3 seconds, it does not take a long time to view, and this check is completed in a short time. There is little burden.
  • the video signal for inspection whose content and occurrence point (time) of the error prepared beforehand are known is input to the video / audio monitoring unit 103 via the sub input unit 102, and the same threshold as described above
  • the video / audio monitoring unit 103 detects an error using
  • the supervisor MN finds the number of correct alarms and the number of slips by collating the contents of the known error with the occurrence part. be able to.
  • the number of passing is extracted and used. Since the video signal for inspection has a length corresponding to a viewing time of several tens of minutes for each content, it does not take time for inspection.
  • the supervisor MN learns from the supervisor input unit 108 the parameter optimization learning from the number of correct alarms, the number of unnecessary alarms, and the number of slips calculated for each of the M video signals. Input to the part 106.
  • T (m) be the number of correct alarms (first event) in the m-th video signal
  • F (m) be the number of unnecessary alarms (second event)
  • the number of slips (third event) be Assuming that H (m), the parameter optimization learning unit 106 obtains an evaluation value A (m) according to the following evaluation function.
  • Wt (m) is the correct alarm weighting
  • Wf (m) is the unnecessary alarm weighting
  • Wh (m) is the passing weight
  • Wt (m)> 0> Wf (m)> Wh (m) 0.
  • Wh (m) -5.
  • the weighting may be fixed for the same type of content. According to this evaluation function, the higher the obtained evaluation value A (m), the more accurate the error detection, and the lower the value, the more inaccurate the error detection.
  • the supervisor MN replaces the default threshold value with the threshold value desired to be changed via the supervisor input unit 108, and executes the same monitoring operation as above using the video signal of the same content.
  • the evaluation value is determined. If the evaluation value of the monitoring operation using the default threshold is equal to or more than the evaluation value of the monitoring operation using the threshold desired to be changed, the parameter optimization learning unit 106 continues using the default threshold in the content. As a thing, do not change the stored default threshold. On the other hand, if the evaluation value of the monitoring operation using the default threshold is lower than the evaluation value of the monitoring operation using the threshold for which change is desired, the parameter optimization learning unit 106 determines the default threshold for the content. It is determined that the change is necessary, and the change is replaced with a desired threshold, that is, learning is performed. Thereby, the accuracy of error detection can be further enhanced.
  • FIG. 6 is a diagram showing a list of examples of a plurality of threshold values when such an update is performed a plurality of times in an inspection item regarding a certain content: freeze, ((a) before update, (b) after update) .
  • a plurality of sets of threshold value groups shown in FIG. 6 are listed up and stored in the built-in memory 106a, displayed on the display unit 107 as needed, and can be confirmed by the supervisor MN.
  • the default threshold group is currently listed at the top with priority 1 but its evaluation value A (m) is “85”.
  • the evaluation value A (m) of the threshold group newly evaluated as the first candidate is "92”
  • the evaluation value A (m) of the threshold group newly evaluated as the second candidate is "81" Suppose that there was.
  • the parameter optimization learning unit 106 since the evaluation value A (m)-"92" of the first candidate threshold group is higher than the evaluation value A (m) "85” of the default threshold group so far, the parameter optimization learning unit 106 However, as shown in FIG. 6B, by setting the priority to 1 by updating, it is replaced as a new default threshold group and transmitted to the video / audio monitoring unit 103 for monitoring. By this, the default threshold group so far will be carried back to priority 2.
  • the parameter optimization learning unit 106 since the evaluation value A (m) “81” of the second candidate threshold group is lower than the evaluation value A (m) “85” of the replaced default threshold group, the parameter optimization learning unit 106 However, by setting the priority to 3 by updating, it will be listed in lower order. The list is stored and used in the memory 106 a of the parameter optimization learning unit 106. Thereby, the threshold value group of high evaluation value A (m) can always be made into the default threshold value group regarding the said content. However, if the content is different, even if the threshold value group uses the same value, the priority may be different. Note that such updating of the threshold value group may be performed automatically by the parameter optimization learning unit 106, or may be performed after waiting for the supervisor MN's permission. Alternatively, regardless of the value of the evaluation value A (m), the threshold designated by the supervisor MN via the supervisor input unit 108 can be set as the default threshold.
  • the first candidate, the second candidate,... are determined, but each time the supervisor MN inputs an arbitrary value from the supervisor input unit 108 to the parameter optimization learning unit 106, the correct answer is obtained each time
  • the evaluation value may be calculated after obtaining the number of alarms, the number of unnecessary alarms, and the number of by-passes.
  • the parameter optimization learning unit 106 individually changes the threshold by + 5% or -5% with respect to the default threshold group, and in each case, the number of correct alarms, the number of unnecessary alarms, and the bypass
  • the evaluation value may be calculated after finding the number of. Evaluation values can be similarly obtained for other monitoring items.
  • the video signal for inspection may be input between the input of the monitoring target video signal, or may be performed in parallel with the input of the monitoring target video signal to perform inspection in the background.
  • the same threshold can then be used to perform error detection.
  • the video / audio storage clip unit 104 cuts out the monitoring target video signal input from the main input unit 101 over a predetermined length at a predetermined timing, and associates it with the monitoring result performed by the supervisor MN.
  • it may be stored as a video signal for inspection in a memory (not shown).
  • the stored test video signal is input from the sub input unit 102 at a necessary timing, and is used for threshold updating as described above.
  • the present invention it is possible to provide a method of adjusting a video monitoring apparatus for appropriately detecting a video and / or an error generated according to content in a digital video signal, and in the digital video signal, the video and / or the error are properly learned It is possible to provide an image monitoring device that detects
  • video monitoring apparatus 101 main input unit 102 secondary input unit 103 video / audio monitoring unit 104 video / audio clip storage unit 105 alarm output unit 106 learning unit for parameter optimization built-in memory 107 a display unit 108 monitor unit 108 monitor input unit MN monitor

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Closed-Circuit Television Systems (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)

Abstract

L'invention concerne un procédé de réglage d'un dispositif de surveillance vidéo destiné à détecter de manière appropriée une vidéo et/ou des erreurs survenant en fonction du contenu de signaux vidéo numériques. L'invention concerne également un dispositif de surveillance vidéo destiné à détecter de manière appropriée une vidéo et/ou des erreurs par apprentissage à partir de signaux vidéo numériques. Le dispositif de surveillance vidéo comporte une unité de surveillance vidéo/audio qui incorpore une mémoire dans laquelle sont stockées des valeurs seuils, et une unité d'apprentissage d'optimisation de paramètres. Le dispositif de surveillance vidéo est configuré pour déterminer qu'une erreur est survenue lorsque l'ensemble d'une pluralité de paramètres variant en fonction de signaux vidéo dépassent des valeurs seuils respectives spécifiées d'après des éléments de surveillance. Au moins deux paramètres parmi la pluralité de paramètres sont associés entre eux, et le dispositif de surveillance vidéo sélectionne un ensemble de groupes de valeurs seuils stockés dans la mémoire en fonction du contenu de la vidéo et/ou de l'audio à surveiller. Par l'intermédiaire d'une analyse de résultats d'avertissement d'erreur, l'unité d'apprentissage d'optimisation de paramètres effectue un apprentissage et met à jour les valeurs seuils sur la base du résultat d'apprentissage.
PCT/JP2017/024350 2017-07-03 2017-07-03 Procédé de réglage d'un dispositif de surveillance vidéo et dispositif de surveillance vidéo WO2019008635A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
PCT/JP2017/024350 WO2019008635A1 (fr) 2017-07-03 2017-07-03 Procédé de réglage d'un dispositif de surveillance vidéo et dispositif de surveillance vidéo
JP2019528202A JP7033797B2 (ja) 2017-07-03 2017-07-03 映像監視装置の調整方法及び映像監視装置
CN201780089397.8A CN110870305B (zh) 2017-07-03 2017-07-03 影像监视装置的调整方法及影像监视装置

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2017/024350 WO2019008635A1 (fr) 2017-07-03 2017-07-03 Procédé de réglage d'un dispositif de surveillance vidéo et dispositif de surveillance vidéo

Publications (1)

Publication Number Publication Date
WO2019008635A1 true WO2019008635A1 (fr) 2019-01-10

Family

ID=64950684

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2017/024350 WO2019008635A1 (fr) 2017-07-03 2017-07-03 Procédé de réglage d'un dispositif de surveillance vidéo et dispositif de surveillance vidéo

Country Status (3)

Country Link
JP (1) JP7033797B2 (fr)
CN (1) CN110870305B (fr)
WO (1) WO2019008635A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021034841A (ja) * 2019-08-22 2021-03-01 日本電気株式会社 監視条件設定装置、システム、方法およびプログラム

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0946733A (ja) * 1995-07-25 1997-02-14 Nippon Hoso Kyokai <Nhk> 映像自動モニタ装置
JP2001269770A (ja) * 2000-03-24 2001-10-02 Kawasaki Steel Corp 溶融金属取扱い設備の異常自動検出方法
JP2006060335A (ja) * 2004-08-18 2006-03-02 Sharp Corp 画像処理装置
JP2006101296A (ja) * 2004-09-30 2006-04-13 Nec Personal Products Co Ltd 映像データ補正装置とその方法、映像出力装置とその方法、再生装置とその方法、プログラムおよび記録媒体
JP2006186787A (ja) * 2004-12-28 2006-07-13 Fujitsu Ten Ltd デジタルデータ受信機
JP2008046346A (ja) * 2006-08-15 2008-02-28 Sony Corp 消費電力削減装置、表示装置、画像処理装置、消費電力削減方法及びコンピュータプログラム
JP2011109183A (ja) * 2009-11-12 2011-06-02 Toshiba Corp 受信状態提示装置
JP2011228975A (ja) * 2010-04-21 2011-11-10 Nikon Systems Inc 映像ノイズ検知装置

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101765025A (zh) * 2008-12-23 2010-06-30 北京中星微电子有限公司 一种监控摄像设备异常检测的系统及方法
CN102131101A (zh) * 2011-04-21 2011-07-20 江苏东怡软件技术有限公司 智能视频图像质量自动分析系统及其分析方法
CN102395043B (zh) * 2011-11-11 2013-09-11 北京声迅电子股份有限公司 视频质量诊断方法
CN102421008A (zh) * 2011-12-07 2012-04-18 浙江捷尚视觉科技有限公司 视频质量智能检测系统
CN103873852A (zh) * 2012-12-11 2014-06-18 上海文广互动电视有限公司 多模态并行的视频质量故障检测方法及装置

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0946733A (ja) * 1995-07-25 1997-02-14 Nippon Hoso Kyokai <Nhk> 映像自動モニタ装置
JP2001269770A (ja) * 2000-03-24 2001-10-02 Kawasaki Steel Corp 溶融金属取扱い設備の異常自動検出方法
JP2006060335A (ja) * 2004-08-18 2006-03-02 Sharp Corp 画像処理装置
JP2006101296A (ja) * 2004-09-30 2006-04-13 Nec Personal Products Co Ltd 映像データ補正装置とその方法、映像出力装置とその方法、再生装置とその方法、プログラムおよび記録媒体
JP2006186787A (ja) * 2004-12-28 2006-07-13 Fujitsu Ten Ltd デジタルデータ受信機
JP2008046346A (ja) * 2006-08-15 2008-02-28 Sony Corp 消費電力削減装置、表示装置、画像処理装置、消費電力削減方法及びコンピュータプログラム
JP2011109183A (ja) * 2009-11-12 2011-06-02 Toshiba Corp 受信状態提示装置
JP2011228975A (ja) * 2010-04-21 2011-11-10 Nikon Systems Inc 映像ノイズ検知装置

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021034841A (ja) * 2019-08-22 2021-03-01 日本電気株式会社 監視条件設定装置、システム、方法およびプログラム

Also Published As

Publication number Publication date
CN110870305A (zh) 2020-03-06
CN110870305B (zh) 2021-06-15
JPWO2019008635A1 (ja) 2020-04-30
JP7033797B2 (ja) 2022-03-11

Similar Documents

Publication Publication Date Title
EP3511862B1 (fr) Système et procédé de classement dynamique de canaux video selon le rang de detection anormale
CN111954026B (zh) 利用基于参考流比较的多匹配检测的媒体频道识别和动作
Series Methodology for the subjective assessment of the quality of television pictures
US8516523B2 (en) Method and apparatus for generation of a preferred broadcasted programs list
US8885942B2 (en) Object mapping device, method of mapping object, program and recording medium
US11410196B2 (en) Managing content delivery via audio cues
US20190026342A1 (en) Electronic data generation methods
JP7048825B2 (ja) より短い時定数を使用した、メディア置換イベント中におけるラウドネスレベルのモニタリング
US20140255004A1 (en) Automatically determining and tagging intent of skipped streaming and media content for collaborative reuse
US20210225409A1 (en) Methods and systems for detection of anomalous motion in a video stream and for creating a video summary
CN110225367A (zh) 一种视频中对象信息的显示、识别方法及装置
CN106028147A (zh) 视频信号监测方法及视频信号监测系统
US20190364088A1 (en) Anomaly detection of media event sequences
Series Methodology for the subjective assessment of the quality of television pictures
US20230336834A1 (en) Systems and methods for aggregating related media content based on tagged content
US20210014562A1 (en) Methods and apparatus to detect boring media
US20040003391A1 (en) Method, system and program product for locally analyzing viewing behavior
US20170024998A1 (en) Setting method and apparatus for surveillance system, and computer-readable recording medium
US20170337639A1 (en) Visual focal point composition for media capture based on a target recipient audience
WO2019008635A1 (fr) Procédé de réglage d&#39;un dispositif de surveillance vidéo et dispositif de surveillance vidéo
GB2438689A (en) Method of detecting correction methods for feature images
US20100054702A1 (en) Information processing device, information processing method, and program
JP2017034514A (ja) 番組分析装置及び番組分析方法
US10349093B2 (en) System and method for deriving timeline metadata for video content
JP4684966B2 (ja) 画像表示装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17916469

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2019528202

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17916469

Country of ref document: EP

Kind code of ref document: A1