Method for video surveillance of open space with fire hazard monitoring

FIELD: physics, video.

SUBSTANCE: invention relates to video surveillance, primarily of open spaces, with fire hazard monitoring and can be used to monitor forest areas in regions with underdeveloped infrastructure. The method comprises video monitoring of a protected area; selecting mobile regions on the obtained image; comparing said regions with reference images from a library of images and making a decision on burning based on the similarity of the obtained images and the available images; wherein mobile objects are selected on the image by breaking down the whole image into rectangular blocks; the shifting of blocks over time is diagnosed from the shift of the centre of a block; separate mobile blocks are merged by spatial clusterisation; time clusterisation of mobile objects is carried out based on a criterion of the intersection of trajectories of separate blocks with each other, wherein the shape of a mobile object is reconstructed by cutting off short edges, joining adjacent boundary points and smoothing the obtained boundaries, wherein the internal region of the object is reconstructed on the obtained boundary, and clusterisation of the selected mobile object is carried out on the normal component of optical flux.

EFFECT: high probability of recognising signs of fire on an open area owing to early detection of fire from smoke traces and air currents.

3 cl, 1 dwg

 

The invention relates to the field of video surveillance, mostly open space, with the control of fire hazards and can be used to monitor forests in regions with poorly developed infrastructure.

There is a method of event detection for video surveillance systems containing the training phase, in which receive a training image of the monitored area at different points in time in the absence of any subject detection events, and working with the discovery phase, in which the current image mentioned areas, while detect the event by comparing the current image with the image corresponding to a linear combination of a set of reference images that approximate the corresponding training image or coincident with them, on the application number 2009106852/08, publ. 10.09.2010,

The known method is effective for situations in which obviously is no want to monitor events that the control of large open spaces it is difficult to guarantee.

There is a method of surveillance that allows you to control the premises and territory to make the video disturbing events and continuous video recording, according to the patent of Russian Federation №2381533, publ. 10.02.2010,

The method is not sufficiently high information content when the work mode is real time.

There is a method of video surveillance, comprising the steps of receiving a video signal of the controlled area by at least one camera, its transmission and its analysis in at least one computing device memory, which according to the invention obtained by the video signal is analyzed using a computing device using information from descriptions of alarm situations, pre-recorded in a database in the memory of the computing device, based on the analysis results receive the information about the purpose, form a control signal, transmit it to a movable camera, adapted for targeting in accordance with the control signal, using the mobile camera receives an image of the target, suitable for further analysis, and transmit it to store in the database.

Preferably in the method according to the invention according to the results of the analysis additionally generate an alarm signal and transmit it to the operator. The analysis is performed with the opportunity to calculate the velocity vector, the current coordinate location of the target, and optionally generate and transmit mobile video camera corresponding control signal to provide guidance in the mobile camera at the target. The control signal transmitted to the movable video camera via the serial communication channel. Analysis of whitefish is Ala from the camcorder performed with the consideration of objectives, selected from the group including brightness, size, duration of stay in the controlled area, the speed and direction of movement. (Patent RF №2268497, publ. 20.01.2006 was adopted for the prototype of the invention).

There is a method of surveillance and control fire hazards, according to which make the monitoring of the protected area, there is obtained the image of the movable area, compare these areas with exemplary images from a library of images and decide to fire on the basis of similarity obtained and available images.

The technical problem to be solved by the invention is to improve the probability of detection of the signs of a fire in an open area due to the early detection of fire smoke trails and streams of air.

The problem is solved by the fact that in the method of surveillance of open space with the control of fire hazards, according to which make the monitoring of the protected area, there is obtained the image of the movable area, compare these areas with exemplary images from a library of images and decide to fire on the basis of similarity obtained and available images

According to the invention the selection in the image of moving objects is done by splitting the image on to akatie blocks, the blocks from shifting over time, diagnosed according to the displacement of the center of the block, a separate moving blocks combine by spatial clustering temporal clustering of moving blocks produced according to the criterion of intersection of the trajectories of individual blocks among themselves, and form a moving object restore cut-off by short edges, connections lying near the boundary points and smoothing the obtained bounds, while the interior of an object to restore the border, and the classification of the selected movable object produced by the normal component of the optical flow. In addition, a library of images formed with the release categories "vehicle", "people", "animals", "the waving branches of the trees", "smoke", "heat trace", "open fire."

In addition, in each of the categories of image library form a subcategory of "trajectory", "speed", "direction", and bind the resulting images to the characteristic features of the protected area.

The invention is illustrated in Fig.1, which presents a block diagram of the proposed method.

An example implementation of the method.

1. The selection in the image of the moving blocks

All the image (position 1 in Fig.1) resb is provided on the blocks B isize n×n pixels. The value of n depends on the object size and image resolution, and is selected from the range from 1 to 100. Assume that the center of the block Biis located at the point (x,y) at time t and at the point (x+Δx, y+Δy) at time t+1. Then the magnitude of the matching blocks and their offsets for two consecutive moments of time are expressed as follows:

D(Bi)=1n2(i,j)Bi|It+1(x,y)-It(x+Δx+i,y+Δy+j)|2,(1)

Δ(Bi)=(Δx)2+(Δy)2 (2)

To eliminate errors of the second kind (false moving blocks), resulting from noise, it is necessary that the value of D has exceeded the threshold δαthat can be determined using a test of significance. For a pixel (x,y), where there is no movement, the value of d(x,y)=It(x,y)-It+1(x,y) represents the noise of the camera. It is known, can be described by a Gaussian distribution with zero mean and variance equal to twice the variance of the noise of the camera. The significance level can be considered as the probability of detecting block as a moving unit, although in reality it is fixed (error of the second kind). If you choose a block size n=16 and put δα=1, then it will fit a probability equal to 0.9 (position 2 in Fig.1).

2. Spatial clustering of moving blocks

At this stage, using clustering techniques are combined separate similar moving blocks, for which the value of D exceeds the threshold value δα, in moving the field. We will consider two blocks are adjacent if they have a common side (or its part) or vertex, i.e. they are related by a 8-adjacency. As characteristic features of BL the Cove, you can use the connectivity C(B iBj), the normalized difference between the matching blocks of D(BiBj) and the normalized block offset Δ(BiBj):

C(Bi,Bj)={1,Bi,Bj-with aaboutwith aednande,andnandhe,(3)

D(Bi,Bj)=|D(Bi)-D(Bj)||D(Bi)+D(Bj)|,(4)

Δ(B i,Bj)=|Δ(Bi)-Δ(Bj)||Δ(Bi)+Δ(Bj)|.(5)

The distance between the moving blocks will be defined as

Dc(Bi,Bj)=C(Bi,Bj)θD(Bi,Bj)+(1-θ)Δ(Bi,Bj)

where the parameter θ is equal to 0.3. Blocks Biand Bjmerged into a single region, if this value is less than threshold δcequal to 0.5 (position 3 in Fig.1).

3. Temporal clustering of moving blocks

The problem with segmentation gaseous objects of the type of smoke is possible nutriwest - at different points in time initially, a single object may consist of several separate areas. In order to identify them as one and the same moving object, will accompany the moving region from frame to frame. Then, while the areas that make up a single object may not touch each other in any frame, the trajectory of these objects will be crossed (position 4 in Fig.1). Thus, the individual objects can be combined in the time domain.

4. Determination of the shape of objects

Obtained in the previous step, the clusters consist of several blocks. To establish the exact shape of the objects used allotment boundary points, for example, by the method of Canny. The obtained edge points for gaseous objects (smoke) are usually not related to each other. To build the closed boundaries of these edges must be applied operations morphology: remove very short edges, as they primarily represent the effect of noise, then use the dilation operation to connect lying near the boundary point, then smooth the resulting borders to give them a more natural appearance.

Let X is a binary the image of the detected edge points. The pixel value equal to 1 means that the pixel is an edge point. The combination of morphological operations to build the boundary of the object from the isolated boundary points as follows:

((XVc)Vd)Vb,(6)

where ⊕ is the operation of dilatation,

(XVc)={x:|(XVc)Vc|>3},

XVb={x:|XVb|3},

Vc=(0000100 00),Vd=(010111010),Vb=(101010101).

The next step is to restore the internal area of the object obtained by the border. If the area of low intensity are very long, even with the help of morphological operations is impossible to restore the border. This means that the detected boundary is open and may not completely include the interior of an object. Therefore, it is necessary to distinguish areas not encompassed obtained abroad, and independent region. To do this, find the intersection with the boundary in the horizontal and vertical directions. Only if the horizontal and vertical lines passing through the pixel, cross the same border, we assume such a pixel belonging to the internal area of the object (position 5 in Fig.1).

5. Classification of selected objects

Classification of selected objects to determine the smoke and fire is carried out using dynamic textures. One of the main methods of classification using dynamic textures is to use optical flow calculation which is a very demanding problem. We will not determine the full optical flow, but only its normal component. This is sufficient for the purposes of the recognition object type and at the same time will avoid a large amount of computation. Normal flow, i.e. the flow in the direction of the intensity gradient ∇I(x,y) is written as follows:

υN(x,y)=-dI/dt||I||n,(7)

wherenis the unit vector in the example is the relation ∇I(x,y). Normal flow contains both temporal and spatial information about dynamic textures: temporal information associated with the motion of boundary points, and spatially with their gradients.

As characteristic features for the classification of objects in addition to the normal flow will also use the maximum value of the directional regularity textures R(i), which would allow an affine invariance of the results. This approach is based on the fact that the regular (periodic) textures are perceived to be so in a wide range of angles. Therefore, certain adequate, the degree of regularity of the considered objects can serve as invariant, physically sound characteristics. We will use polar coordinates on the grid αi=Δα·i, dj= ∆ D·j.

Determine the maximum value of the directional regularity R(i)MR=maxαiR(αi). The value of MRis in the range 0≤MR≤1, where MR=0 corresponds to random textures species, a MR=1 meets the textures are well pronounced regularity; it Vici which is for a set of overlapping Windows, completely contains the region of the object obtained in the previous step, and selects the maximum value ofP=maxWMR(W). Here W is marked window, the size of which is determined in such a way as to include at least two periods of contrast. Because the procedure is repeated for several consecutive frames, thus we obtain the time dependence P(t).

As characteristic features of the textures will use the following averaged over all frames of the sequence of values:

- Average values ofdivυN(x,y)androtυN(x,y).

- The ratio of the average values of the normal flow and its standard deviation.

- The degree of uniformity of the orientation of the normal flowφ=||(x,y)ΩυN(x,y)||(x,y)Ω||υN(x,y)||,

where Ω is the set of pixels where the value of the normal flux is non-zero.

- The average value of P(t).

The variance of P(t).

All these quantities are invariant to translation and rotation. The identity of the object to the class of objects corresponding to the smoke on the image may be determined using any method of classification, such as support vector machines or neural networks (item 6 in Fig.1).

1. Method of surveillance of open space with the control of fire hazards, according to which make the monitoring of the protected area, there is obtained the image of the movable area, compare these areas with exemplary images from the image library and accept solved the e on fire on the basis of similarity obtained and available images, characterized in that the selection in the image of moving objects is done by splitting the image into rectangular blocks, offset blocks, with time, diagnosed according to the displacement of the center of the block, a separate moving blocks combine by spatial clustering temporal clustering of moving blocks produced according to the criterion of intersection of the trajectories of individual blocks among themselves, and form a moving object restore cut-off by short edges, connections lying near the boundary points and smoothing the obtained bounds, while the interior of an object to restore the border, and the classification of the selected movable object produced by the normal component of the optical flow.

2. The method according to p. 1, characterized in that the image library form with the release categories "vehicle", "people", "animals", "the waving branches of the trees", "smoke", "heat trace", "open fire."

3. The method according to PP.1 and 2, characterized in that in each of the categories of image library form a subcategory of "trajectory", "speed", "direction", and bind the resulting images to the characteristic features of the protected area.



 

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