CN102970517A - Holder lens autonomous control method based on abnormal condition identification - Google Patents
Holder lens autonomous control method based on abnormal condition identification Download PDFInfo
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Abstract
The invention relates to the technology of video monitoring, and in particular relates to a holder lens autonomous control method based on abnormal condition identification with image intelligent analysis. The method mainly comprises the steps of: dividing a rotating range of the holder lens into a plurality of blocks to obtain the characteristic vectors of each block; obtaining current characteristic vectors of each block and comparing the current characteristic vectors with corresponding normal characteristic vectors to obtain a covariance; judging whether the video image of the current block is abnormal or not; locking the abnormal target; automatically tracking the abnormal target by the holder lens and driving the holder lens to rotate immediately by a predicting manner. The method provided by the invention has the beneficial effects that abnormal conditions are automatically sensed by the video image analyzing technology and the movement tracks of the abnormal conditions are analyzed and predicted, and then the holder lens is automatically controlled to rotate to track the abnormal target in real time, so that monitors can execute monitoring tasks more efficiently and rapidly. The method provided by the invention is particularly suitable for a holder lens monitoring system.
Description
Technical field
The present invention relates to Video Supervision Technique, particularly relate to a kind of platform-lens of identifying with the anomaly-based sight of image intelligent analysis and monitor method from master control.
Background technology
Along with the development of computer soft or hard technology, be widely used in every field based on the video monitoring system of multimedia technology.In present many markets, bank and the high-grade residential quarter platform-lens supervisory control system has been installed all, some industrial and mining enterprises, bank vault and military depot also wish to utilize the platform-lens supervisory control system to realize intellectuality, automation unattended operation.Therefore the platform-lens supervisory control system of intelligence has very large market.Present platform-lens supervisory control system is monitor staff's persistent surveillance screen normally, and the target that enters guarded region is finished motion tracking to target by the operation keyboard rocking bar.And when target moves away from video camera, also need to control manually the camera lens zoom, target is carried out feature amplify.In manually controlling the real-time tracking and zoom process of platform-lens realization to target, also may there be artificial tracking error.Therefore, the present technology of utilizing platform-lens to monitor exists degree of intelligence lower, the situation of error occurs easily.
Summary of the invention
This reality invention technical problem to be solved is, is exactly for the lower problem of present platform-lens monitoring intelligent degree, proposes a kind of autonomous control method of platform-lens of anomaly-based sight identification.
The present invention solves the problems of the technologies described above the technical scheme that adopts: the autonomous control method of platform-lens of anomaly-based sight identification, it is characterized in that, and may further comprise the steps:
A. the slewing area with platform-lens is divided into several blocks, and the video image collection of each block under normal sight is set to an independent sample collection, with the gray scale of each pixel in each sample image characteristic vector as the independent sample collection;
B. gather the video image of current each block by platform-lens, obtain current block video image characteristic vector and compare with the characteristic vector of corresponding independent sample collection, obtain covariance;
C. judge according to covariance whether the video image of current block is in the early warning interval, if, then enter steps d, if not, then get back to step b;
D. obtain the video image of current block and the gray scale difference value of corresponding independent sample collection, gray scale difference value is locked as abnormal object greater than the zone of set point continuously, platform-lens carries out automatic tracing to abnormal object;
E. the video image of abnormal object block appears in continuous acquisition, and the movement locus of abnormal object is predicted, drives platform-lens according to predicting the outcome and in time rotates to reduce lag time.
Concrete, described independent sample collection comprises 100 video images at least.
Concrete, among the step a with the gray scale of each pixel in each sample image as the concrete steps of the characteristic vector of independent sample collection be:
A1. with the gray scale of each each pixel of sample image as characteristic vector, input neural network is trained;
A2. the output of neural net is vectorial at the gray feature of normal contextual model as this block.
Concrete, described neural net is the RBF radial base neural net.
Beneficial effect of the present invention is, when being implemented in Real Time Monitoring, by the unusual sight of video image analysis technology Auto-Sensing, and its movement locus of analyses and prediction, then it rotates with the real-time tracking abnormal object platform-lens from master control, thereby makes the monitor staff more carry out monitor task in efficient quick ground.
Description of drawings
Fig. 1 is method flow diagram of the present invention.
Embodiment
Be described further below in conjunction with the method for accompanying drawing to invention:
As shown in Figure 1, the present invention proposes a kind of autonomous control method of platform-lens of anomaly-based sight identification, key step is: at first the slewing area with platform-lens is divided into several blocks, the video image collection of each block under normal sight is set to an independent sample collection, with the gray scale of each pixel in each sample image characteristic vector as the independent sample collection, generally in order to ensure monitoring range, can divide the block more than 3; Gather the video image of current each block by platform-lens, obtain current block video image characteristic vector and compare with the characteristic vector of corresponding independent sample collection, obtain covariance; Whether the video image of judging current block according to covariance is in the early warning interval, usually the early warning interval can be arranged on [30, + 30] between, if, then continue to obtain the video image of current block and the gray scale difference value of corresponding independent sample collection, with gray scale difference value continuously greater than set point regional as, platform-lens carries out automatic tracing to abnormal object, if not, then continue to gather the video image of each block; Obtain the video image of current abnormal object block and the gray scale difference value of corresponding independent sample collection, gray scale difference value is locked as abnormal object greater than the zone of set point continuously, here also blocked abnormal object can be placed the centre position of display interface, with better prompting observer, and usually set point can be set as between the 40-80, platform-lens carries out automatic tracing to abnormal object; The video image of abnormal object block appears in continuous acquisition, and the movement locus of abnormal object predicted, drive platform-lens according to predicting the outcome and in time rotate to reduce lag time, here used prediction algorithm can be recurrent least square method, also can keep all the time the centre position that abnormal object is placed display interface.
Concrete, described independent sample collection comprises 100 video images at least.
A kind of concrete gray scale with each pixel in each sample image as the concrete steps of the characteristic vector of independent sample collection is: at first with the gray scale of each each pixel of sample image as characteristic vector, input neural network is trained; Then the output of neural net is vectorial at the gray feature of normal contextual model as this block.
Concrete, described neural net is the RBF radial base neural net.
Claims (4)
1. the autonomous control method of platform-lens of anomaly-based sight identification is characterized in that, may further comprise the steps:
A. the slewing area with platform-lens is divided into several blocks, and the video image collection of each block under normal sight is set to an independent sample collection, with the gray scale of each pixel in each sample image characteristic vector as the independent sample collection;
B. gather the video image of current each block by platform-lens, obtain current block video image characteristic vector and compare with the characteristic vector of corresponding independent sample collection, obtain covariance;
C. judge according to covariance whether the video image of current block is in the early warning interval, if, then enter steps d, if not, then get back to step b;
D. obtain the video image of current block and the gray scale difference value of corresponding independent sample collection, gray scale difference value is locked as abnormal object greater than the zone of set point continuously, platform-lens carries out automatic tracing to abnormal object;
E. the video image of abnormal object block appears in continuous acquisition, and the movement locus of abnormal object is predicted, drives platform-lens according to predicting the outcome and in time rotates to reduce lag time.
2. the autonomous control method of platform-lens of anomaly-based sight identification according to claim 1 is characterized in that described independent sample collection comprises 100 video images at least.
3. the autonomous control method of platform-lens of anomaly-based sight according to claim 1 identification is characterized in that, among the step a with the gray scale of each pixel in each sample image as the concrete steps of the characteristic vector of independent sample collection is:
A1. with the gray scale of each each pixel of sample image as characteristic vector, input neural network is trained;
A2. the output of neural net is vectorial at the gray feature of normal contextual model as this block.
4. the autonomous control method of platform-lens of anomaly-based sight identification according to claim 3 is characterized in that described neural net is the RBF radial base neural net.
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| CN113610816B (en) * | 2021-08-11 | 2024-06-14 | 湖北中烟工业有限责任公司 | Automatic detection and early warning method and device for transverse filter rod and electronic equipment |
| CN113992894A (en) * | 2021-10-27 | 2022-01-28 | 甘肃风尚电子科技信息有限公司 | Abnormal event identification system based on monitoring video time sequence action positioning and abnormal detection |
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