JP2005011057A - Spatial information distribution device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To acquire detailed dynamic information, which cannot be grasped by image information of a camera and the like alone, about a person/object and to merge the detailed dynamic information for performing display according to a level and distributing the merged information. <P>SOLUTION: This spatial information distribution device extracting characteristics from input information for detecting an abnormal condition by the input information is provided with a characteristic quantity extraction part 1a extracting characteristics of inputted image information and identification information obtained from individual detection objects, a sensor cooperation part 2a merging the image information and the identification information together, a distribution part 4a distributing characteristic quantity of spatial information obtained by merging, and a spatial information visualizing part leveling a type of the spatial information according to the degree of detail and display contents for acquiring leveled visible information, and the leveled visible information is distributed from the distribution part. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a spatial information distribution device that collects and analyzes information on a space based on a plurality of information with a camera and other sensors, and distributes the spatial information according to the receiving side's state and request. Is.
[0002]
[Prior art]
As an example of a conventional information distribution apparatus, for example, according to Patent Document 1, there is an abnormal event detection apparatus that detects an abnormality on a road. According to this device, a road is photographed with a camera, and by analyzing the image, it is possible to measure the speed and number of vehicles, and to detect a stop, etc., and correct according to the degree of congestion.
Local synthesis that has speed, number measurement, abnormal running monitoring function, stop object detection, and congestion level measurement function, and corrects data related to speed and number obtained by speed, number measurement and abnormal driving monitoring function according to the congestion level It has a judgment part.
Furthermore, the background difference feature is extracted, the vehicle is tracked, and as a result, the traffic flow is measured (number, speed). Further, the feature of the frame is extracted from the input image, and the stopped vehicle is detected. Also, features of spatial differentiation and time differentiation are extracted from the input image, and the degree of congestion is measured. These results are comprehensively determined by the local comprehensive determination unit, and the presence or absence of an abnormal event is determined.
[0003]
By plotting the degree of congestion at each point in a plane and capturing the degree of change in the degree of congestion and the degree of congestion for each lane spatially, abnormalities in areas outside the camera's field of view are estimated.
In the case of traffic jams in all sections in the camera field of view, it is judged as chronic traffic jams if there is an abnormality outside the measurement range, or where chronic traffic jams occur. If there is only a part of the traffic in the measurement range, it is judged as abnormal.
Abnormal driving, detection of falling objects, and the like can be performed by examining a rapid change in speed in which range the vehicle is traveling with respect to the lane.
[0004]
[Patent Document 1]
JP-A-6-76195
[0005]
[Problems to be solved by the invention]
The conventional abnormal event detection apparatus is configured as described above, and since only the image (video) from the camera is used as the information source, it was possible to detect the car that can be photographed and capture the movement of the car. It cannot cope with human movements and movements that are difficult to extract by analysis alone. In particular, there is a problem that it is difficult to grasp the movement of each person when the number of people reflected on the camera is large.
In addition, even if it can capture macro movements of the car such as road congestion, it cannot respond to micro movements such as individual movements.
[0006]
The present invention has been made in order to solve the above-described problems. It is an object of the present invention to acquire individual detailed dynamic information that cannot be grasped only by a camera, visualize it as useful information, and use it.
[0007]
[Means for Solving the Problems]
The spatial information distribution device according to the present invention is a device that extracts features from input information and detects an abnormality from the input information.
A feature amount extraction unit for extracting features of the input video information and identification information;
Sensor cooperation unit that fuses this video information and identification information,
And a distribution unit that distributes feature quantities of the spatial information obtained by the fusion.
[0008]
DETAILED DESCRIPTION OF THE INVENTION
Embodiment 1 FIG.
FIG. 1 is a diagram showing an example of a system using the spatial information distribution apparatus according to Embodiment 1 of the present invention. In the figure, in addition to the video information from the wireless camera 8, a feature amount extraction unit for newly receiving information from the wireless IC tags 6 a and 6 b and information from the GPS portable terminal 7 a equipped in a portable terminal (phone) or the like. 1a, and a plurality of pieces of information are merged by the sensor cooperation unit 2a, and the result is stored in the latest database (DB) 12a.
Here, the term “fusion” refers to combining after analyzing information such as an image from a camera and an ID from an IC tag.
The fused information is distributed to the user. At that time, the spatial information distribution unit 4a determines the user to be distributed according to the content and status of the information, and further determines the terminal of the distribution destination, the communication status, etc. The spatial information visualization unit 5a is requested to generate appropriate visualization information, and the visualization information acquired from the spatial information visualization unit 5a is distributed to the user.
[0009]
Next, the operation will be described. This will be described based on the processing flow of FIG.
The wireless IC tag in FIG. 1 is attached to a person who is permitted to enter a certain area such as a company. The IC tag transmits each ID.
In addition, a wireless camera and a wireless base station device are installed at certain intervals or in places where it is desired to monitor the passage of people.
The radio base station apparatus receives the ID of the IC tag of the person who has passed, and sends it to the center in step 100a. Also, the video from the wireless camera is sent to the center in step 100c.
For example, the information of the IC tag 6a possessed by a person who has passed near the area X is transmitted to the center spatial information distribution device 21a via the radio base station device 20a, and further the video of the wireless camera 8a is transmitted.
Similarly, information on the IC tag 6b possessed by a person who passes through the area Y via the radio base station apparatus 20b and video information on the wireless camera 8b are transmitted.
[0010]
In the spatial information distribution device 21a, the feature amount extraction unit 1a extracts the ID of the IC tag and the feature amount of the video transmitted in step 101.
The ID of the received IC tag and the entry / exit time are recorded for each area, and are stored in the latest DB 12a as shown in FIG. This can be used as route information for each person.
Further, the number of IC tags recognized every time (for example, every second), that is, the number of people who have passed is counted and accumulated.
From the camera video, the motion of a person who has passed is extracted by recognizing a moving object in the image using a known technique for extracting a person. By extracting the motion vector of the object, the speed and direction of the motion can be specified. Similarly to the case of the IC tag, the number of passing people can be counted every time (for example, every second) from the motion vector of the object.
From these pieces of information, it is possible to count the number of people who have passed, and it is possible to acquire macro dynamic information such as, for example, that there are many people who pass through, there are many people who are staying, and there are no people coming and going.
The feature amount obtained here is accumulated in the latest DB 12a in step 101a.
[0011]
In addition, if a position detection sensor such as GPS and a mobile terminal 7a having a wireless function are registered in advance, when the wireless base station device 20 is passed, a request for position information transmission is made from the wireless base station device. Then, by transmitting the position information captured by the GPS function of the mobile terminal (telephone) to the radio base station apparatus 20, it is possible to grasp the position information in step 100b even if no IC tag is attached.
[0012]
The sensor cooperation unit 2a analyzes information from a camera, a sensor, and the like, and then fuses the information on the time axis in step 102.
By the way, an IC tag has a difference in the receivable distance depending on the difference in frequency, the sensitivity of the receiver and the output of the transmitter. In addition, the range that the camera captures as an image changes depending on the direction in which it is facing, zoom, resolution, and the like.
FIG. 4 shows an example of an area where the camera field of view and IC tag information can be acquired.
Records the data of people who have passed since the start because the size of the above area is different depending on the performance of the IC tag used in the place, the performance of the camera, installation conditions, the direction in which the person with the IC tag enters. Thus, the deviation between the IC tag and the camera is automatically calculated. Also, every time the system is switched, the deviation between the IC tag and the camera is automatically calculated.
[0013]
The automatic calculation of the deviation between the IC tag and the camera will be described below.
At this time, it is necessary to first recognize and classify the direction in which the person travels.
The latest DB 12a holds the position information of each wireless base station and the position information of the camera. Furthermore, the entry / exit information of each IC tag is held for each area. As a result, the route of each IC tag can be known, and the direction in which it will proceed can be predicted to some extent.
For example, when area X, area Y, and area Z are on a straight line as shown in FIG. 4 and a tag 0812 is walking in the direction of area Z and area Y and exits area Y, From the direction, it can be predicted that the possibility of going to the area X on the opposite side of the area Z is high.
If 0812 is actually received in the area X, it can be estimated that the IC tag comes from the direction of the area Y.
Check the direction and entry / exit times of multiple people's IC tags, and proceed by averaging multiple probable data, excluding abnormal data that protrudes based on the distance and positional relationship between wireless base stations. Record directions and entry / exit times.
Furthermore, by combining with the recognition information from the camera, the deviation for each sensor is calculated.
[0014]
The above will be described in detail.
FIG. 5 shows an example in which the timing captured by the camera and the radio base station is represented on the time axis when a person with tags A and B moves in opposite directions as shown in FIG.
FIG. 5A shows how the sensor and the camera each capture a person who moves in the direction A, which is the right direction in FIG. 4, and FIG. 5B shows a case where the person moves in the direction B, which is also the left direction. Indicates. FIG. 5A and FIG. 5B show that the timing at which the IC tag is received and the timing at which it is captured by the camera are different depending on the direction in which the person comes. When a person enters the IC tag reception range E2 in the direction of FIG. 5A, the shift P is not large because it is close to the camera visual field E1, but in the case of FIG. Since the distance is farther than E1, the deviation R is large.
On the other hand, when leaving the area, it can be seen that the deviation S in FIG. 5B is smaller than the deviation Q in FIG.
[0015]
As described above, in each of the directions (A) and (B), the average value of the shift time at the time of entry and exit of the time captured by the tag and the camera is obtained.
Using this shift time, the ID captured by the base station is associated with the person captured by the camera.
For example, consider a case where a plurality of persons enter the area E2 with a slight shift. Based on the time when each tag entered E2, the time when the person was captured by the camera, the captured position, the direction of movement of the person, the speed, and the calculated "deviation time", Corresponds to ID.
In this way, by associating the ID captured by the base station with the person captured by the camera, the dynamic information on the spot can be collected and analyzed. In addition, it is possible to recognize each person even when the face cannot be recognized because the face cannot be clearly seen on the camera image.
In the example, only the two directions A and B have been described. However, it is possible to set and calculate three or more directions for calculating the “deviation time” by examining the movement, so that the correspondence is highly accurate. Is possible. However, it is necessary to increase the number of people who initially accumulate data. In addition, the calculation amount increases.
In addition, it is possible to set the number of persons for measuring movements or the time for collecting data in order to measure the deviation times P, Q, R, and S.
In order to specify the moving direction of the person, a means for analyzing the image of the camera video can also be used.
Furthermore, adjustments can be made in the same manner with respect to information obtained by other means such as acquisition of the position of the mobile phone by GPS.
[0016]
In addition, if the number of people specified by the IC tag and the number of people specified by the camera are different, and the number of people recognized by the camera is small, it may not be recognized on the image, such as being a shadow of another person, and will be counted. It may be a case of damage.
On the other hand, if there are more people recognized by the camera, there is a possibility that a person without an IC tag has passed. However, if the camera video is analyzed and it is possible to determine that a person has moved from a certain place, it is possible to determine that a suspicious person who does not have an IC tag and does not have permission to enter has passed.
[0017]
There are cases where there are many people who cannot identify the exact number of people because the number of people is large and the camera cannot identify the exact number of people, or the face of the overlapping person cannot be detected depending on the angle of the camera. However, in the above embodiment, there is an effect that the data can be corrected by effectively using the information of the IC tag and fusing it with the information of the camera and various sensors. Can be obtained. Even when the cardboard boxes are overlapped and moved, if the IC tag is attached to the cardboard box and the ID information of the IC tag can be received, each box can be recognized. For example, a temperature sensor can monitor both box temperature changes and box movement. That is, if a plurality of types of sensors are used in combination, the temperature can be made a part of the identification information.
In addition, face authentication of registered persons has become possible, but even if the face of each person cannot be identified due to wearing sunglasses or a mask, who passes through the ID information of the IC tag? It is possible to acquire information about each person's movements as well as macro movements.
[0018]
In this way, by integrating information from various sensors such as a camera and an IC tag, it is possible to acquire dynamic information that may be missed by the camera or the sensor alone.
There are various temperatures such as room temperature, air temperature, body temperature, etc. as sensor identification information. In addition, frequency distribution such as radio waves and infrared, intensity, pressure such as atmospheric pressure, water pressure, etc. , Magnetic field distribution, strength, wind direction, speed, water level, flow rate, flow velocity, sunshine / sunshine, rainfall, snow cover, seismic, tilt, etc. Sensing information can be targeted.
For example, in a room where an air conditioner is operating, various sensors attached to the wall measure the temperature, temperature, direction of wind, amount of sunshine and solar radiation, etc. The speed and direction are monitored, and the temperature of the person's body is monitored by infrared rays.
In this manner, various sensing information in the three-dimensional space is acquired and monitored, and information necessary for detecting an abnormal part or returning to a comfortable state is distributed according to the situation.
In addition, the number of people is usually counted by a sensor, and as a result, only when it is determined that there is a possibility of abnormality, the image processing at that time is confirmed by image processing, thereby reducing the image processing load. The method of thinking is also conceivable.
The camera and the wireless base station device may not be installed at the same interval. In that case, adjustment is possible by the camera transmitting its position wirelessly to the wireless base station device.
[0019]
Next, the fusion information of various sensors obtained in this way is visualized and distributed.
In the spatial information distribution unit 4a, the level to be visualized is set according to the terminal of the distribution destination and the communication status, and the visualization is instructed to the spatial information visualization unit 5a.
For example, when 3 levels of visualization are set, the A level information described below is used as the Internet information for a distribution destination with a wide band and a large display like the security monitoring center. Similarly, C level information is also distributed to portable terminals that distribute B level information wirelessly.
The spatial information distribution unit 4a also has a function of converting video data according to a terminal to be distributed and communication circumstances even when the same video is transmitted.
[0020]
In the visualization in step 105 in the spatial information visualization unit 5a, for example, the graph of the number of passing persons in each area is mapped to each area on the map.
B level: The number of passing people is displayed in a graph only for the area selected by the user on the map.
C level: Displayed in numbers instead of the graph in the selected area.
[0021]
In Embodiment 1 above, the movement of people and things can be captured by sensors and cameras, and useful information can be acquired and distributed by coordinating each data. Various effects as described above can be expected.
In addition, since the acquired information is visualized and distributed, it is easier to judge the situation instantaneously than when sending information only with characters.
[0022]
Embodiment 2. FIG.
FIG. 6 shows a configuration diagram of the second embodiment. Compared to the first embodiment, the feature amount extraction unit and the center cooperation unit are installed in the base stations in each area, and the spatial information distribution unit receives, stores, and analyzes and distributes the information after fusion. To do.
In each area, a base station 22 is installed together with a wireless camera. The base station 22 is provided with a radio base station device 20c, a feature amount extraction unit 1c, and a sensor cooperation unit 2c.
[0023]
The basic operation of each device and unit is almost the same as in the first embodiment, but the feature amount extraction unit 1c and the sensor cooperation unit 2c are distributed and installed in local base station devices instead of the center. Is done.
In each area, information from the sensor and camera received by the radio base station apparatus 20c is extracted by the feature amount extraction unit 1c. The sensor cooperation unit 2c fuses the sensor information.
[0024]
The merged sensor information is sent from the base station 22 to the center spatial information distribution device 21b. As the sensor information, information on the number of people passing through each place, such as Area A and Area B, and the time of passage are sent. In the spatial information visualization unit 5b of the spatial information distribution device 21b, the number of passing people for each area is visualized for each time, and distributed from the spatial information distribution unit 4b to the user.
When the communication capacity is small, it is possible to distribute to a mobile phone with a wireless function that passes nearby without passing through the spatial information distribution device 21b.
In the second embodiment, feature extraction and sensor cooperation are performed in each area, thereby reducing the amount of communication for transmitting video and the like to the center. Therefore, it is effective when the communication capacity is small. It is also possible to directly distribute information obtained by the base station device to a wireless mobile phone or the like without going through the center.
[0025]
Embodiment 3 FIG.
The configuration is shown in FIG. 1 which is the same as in the first embodiment. The operation in each unit is almost the same as in the first embodiment, but the operation of the sensor cooperation unit is different. In the sensor cooperation unit, not only temporal data fusion but also spatial fusion can be performed.
For example, the feature amount extraction unit 1a of the spatial information distribution device 21a calculates the speed of a moving person or object from analysis of a sensor or camera video.
In the sensor cooperation unit 2a, the data of each area is spatially fused. For example, only in a certain area, when the passing speed is abnormally slow compared to other areas, it is determined that something is abnormal. If it is determined that there is an abnormality, the video from a nearby video camera is distributed to the monitoring center or the mobile phone of the monitoring person to warn.
[0026]
As described above, since information from each sensor is captured as spatial information, it is not possible to know only by independent information in each area. It is possible to detect an abnormal state or the like occurring in
Further, the wireless tag can know which ID wireless tag is near to which base station by transmitting the ID and receiving it at the base station, but the detailed position information is unknown.
For example, if the wireless tag 0195 is received by the wireless base station device 20a of FIG. 4, it can be seen that ID = 0195 is within the range of E2, but the position information with higher accuracy is unknown.
However, since the sensor cooperation unit 2a can calculate the timing when the person enters the range E1 captured by the camera, the information including the information about the traveling direction of the person, which is known from the tag route information stored in the latest DB 12a, Can be identified. As a result, it is possible to narrow down the pinpoint information from the rough position information of the range E2.
[0027]
From the same operation, the sensor cooperation unit 2a also has a function of specifying a human face on the camera image.
By adjusting the timing of entering the wireless tag reception range E2 and the timing captured by the camera, the ID of the person reflected in the camera can be specified.
Further, when there is a large deviation from this timing and the number of people reflected on the camera does not match the number of IC tags, it can be determined that the person is a suspicious person without an IC tag.
If you know that there is a person who does not have an IC tag in multiple areas, you can issue a warning that the person's face may be a suspicious person, and by checking the route, The direction of travel can be estimated and tracked.
As described above, by spatially interpolating information from nearby base stations and information obtained from sensors and cameras, it is possible to predict and track a person's direction of travel, and to narrow down accurate position information. Can be drunk.
[0028]
Next, the operation of the spatial information visualization apparatus will be described.
In the first embodiment, it is described that the number of passing people is displayed in a graph for each area by visualization at the A level, but it is also possible to visualize so that the degree of congestion can be easily grasped spatially.
The change in the number of people passing is shown in FIG. The congestion level of people who pass through can be expressed on a graph-by-area basis, but the overall flow is difficult to understand instantly. Therefore, the number of people is classified as “small”, “average”, “large”, or “abnormal”.
The large number of people is represented on the map by the difference in line thickness and density. If the flow direction is expressed in a different color, it is still easy to understand. By performing such visualization, it is easier to determine at a glance dynamic information in the space, such as from where to where people are flowing, compared to information independent of each area.
FIGS. 8A, 8B, and 8C show examples in which visualization is made in this way and the contents of visualization are changed depending on the terminal of the distribution destination and communication circumstances.
8A is a diagram in which the degree of congestion is visualized by line density, FIG. 8B is a diagram in which the degree of congestion is visualized by line thickness, and FIG. 8C is an example of delivery to a mobile phone. FIG.
In the third embodiment described above, since the spatially merged information is visualized, it is easier to determine the situation in the entire space than when receiving independent information such as information in each area.
In the first embodiment, in determining the level of visualization, the degree of congestion, which is the number of people, is used as a parameter. However, the direction and position of a motion vector of a person or an object on the video, its time zone, weather, or the like As described above, it is also possible to set based on the conditions of a plurality of parameters.
FIG. 7 is a two-dimensional graph. The visualization method is divided by providing a level to this graph. However, visualization can be set based on three-dimensional information.
In the first embodiment, the level is classified into four levels, but it is also possible to change the contents of visualization into more levels such as several tens of levels as necessary.
[0029]
Embodiment 4 FIG.
A configuration in the present embodiment is shown in FIG.
Compared with the configuration of FIG. 1, this configuration includes a feature amount analysis unit 3 between the sensor cooperation unit 2 and the spatial information distribution unit 4. The feature amount analysis unit 3 performs pattern analysis using the extracted feature amount. The latest DB 12 stores the feature amount fused by the sensor cooperation unit 2 and the feature pattern that is the result of the feature amount analysis unit 3. The accumulation database 13 stores patterns of accumulated data so far.
[0030]
Next, the operation will be described with reference to FIG.
For example, when a visitor to a certain station is examined, patterns of other parameters are analyzed using time as a key, and past accumulated data is classified into patterns.
If the change in the number of people per time is obtained as shown in FIG. 7, for example, the number of people is “small”, “average”, “large”, “abnormal”, and the time is “T1” to “T9”. Classify into:
In the case of FIG. 7, the time zone of T2 has the largest number of people in the morning, and the time zone of T8 has the most time in the day. T3 has the least number of people in the morning, and T6 has the least number of people in the day. I understand that.
Similarly, by performing pattern analysis on the average speed, etc., it is possible to perform pattern analysis such that time zone T2 has many people and fast speed, but T8 has many people but slow speed. Become.
In addition, it is possible to perform pattern classification considering various conditions by adding various elements such as the direction of movement, the sex of the moving person, the weather, and the like.
In this way, the accumulated data is subjected to pattern analysis and the obtained pattern is stored.
[0031]
Pattern analysis is also performed on data obtained in real time and compared with the pattern of accumulated data. More specifically, the comparison with the pattern here means that the result of a certain analysis process such as classification or averaging of the result of accumulated data according to a certain parameter value is compared with the obtained data. Say. Alternatively, it means that the obtained data is processed and compared with the processing result of the accumulated data.
For example, many people entered at times when there were usually few people going in and out, many people entered and exited abnormally compared to the usual average number of people, and the movement speed was fast, a direction different from the direction that ordinary people should go When a large number of people advance, or an unusually large number of people move together in one place, the patterns are classified based on multiple parameter conditions and rarely occur Warn.
It is also possible to make the warning level variable based on pattern differences, parameter threshold information, and the like.
Although an example in which pattern classification is performed using the time zone and congestion degree graph of FIG. 7 used for determining the visualization level is shown, parameter information for determining the visualization level and parameter information for pattern classification are as follows: It can be completely different.
FIG. 7 shows a two-dimensional graph, but patterns can be classified based on three-dimensional information.
Moreover, in the case of a car, etc., there are cases where many cars meander in a place where they do not meander, or the car suddenly increases its speed and starts running. In the case of meandering, there may be an obstacle there, so confirmation can be obtained by sending the captured image to the monitoring center.
[0032]
By performing simple pattern analysis and analyzing the appearance frequency of patterns, etc., patterns such as normal patterns, regularly occurring patterns, and rare patterns are analyzed. Alternatively, in addition to the appearance frequency, a statistical method is used to detect an attention pattern such as an abnormal pattern or a rare pattern.
In addition to such automatic determination, it is also possible to set a pattern that is abnormal by a human as a result of pattern classification.
The data stored as real-time data is set for one day, one week, etc. according to the usage and situation. The accumulated data is data that is accumulated every time the real-time data becomes old and is stored as needed. It is assumed that the pattern analysis of the accumulated data is periodically performed once a week or once a month depending on the setting.
In the above-described example, the patterns are classified into the patterns 2 and 3. However, it is possible to classify them into more detailed levels as required.
[0033]
The current state is determined by classifying only the accumulated data and assigning real-time data thereto.
Another method is to classify patterns by mixing real-time data with accumulated data. The latter can be classified with higher accuracy but takes more time.
[0034]
In the fourth embodiment, since the configuration is as described above, information from various sensors such as a camera and an IC tag is collected, pattern analysis is performed, and the monitored situation is abnormal or noticeable compared to usual. You can decide what to do and issue a warning.
Moreover, future prediction can be performed by comparing with cumulative data.
[0035]
【The invention's effect】
As described above, according to the present invention, it is provided with a feature amount extraction unit that simultaneously extracts features of input video information and identification information, a sensor cooperation unit that combines these, and a distribution unit that distributes the combined spatial information. Therefore, there is an effect that the accurate information of the identification target can be grasped and distributed by level.
[Brief description of the drawings]
FIG. 1 is a diagram showing a configuration of a spatial information distribution apparatus and a system using the same in Embodiment 1 of the present invention.
FIG. 2 is a diagram showing a processing operation flow performed by the spatial information distribution apparatus in the first embodiment.
3 is a diagram illustrating an example of identification tag data included in the latest database according to Embodiment 1. FIG.
FIG. 4 is a diagram illustrating an input information range obtained by a sensor cooperation unit according to the first embodiment.
FIG. 5 is a diagram illustrating an example of input information obtained by the sensor cooperation unit according to the first embodiment.
FIG. 6 is a diagram showing a configuration of a spatial information distribution apparatus and a system using the same in the second embodiment of the present invention.
[Fig. 7] Fig. 7 is a diagram of an example of spatial data indicated by the passage of time distributed by the spatial information distribution device according to Embodiment 3 of the present invention.
FIG. 8 is a diagram illustrating an example of spatial data in a predetermined area distributed by the spatial information distribution device according to the third embodiment.
FIG. 9 is a diagram showing a configuration of a spatial information distribution apparatus and a system using the same in the fourth embodiment of the present invention.
FIG. 10 is a diagram showing a processing operation flow performed by the spatial information distribution apparatus in the fourth embodiment.
[Explanation of symbols]
1, 1a, 1c Feature extraction unit, 2, 2a, 2c Sensor cooperation unit, 4, 4a, 4b Spatial information distribution unit, 5, 5a, 5b Spatial information visualization unit, 6, 6a, 6b IC tag, 7, 7a GPS Mobile terminal, 8, 8a, 8b Wireless camera, 12, 12a Latest database, 13 Cumulative database, 20a, 20b, 20c Wireless base station device, 21, 21a, 21b Spatial information distribution device, 22, 222a, 222b Base station.

Claims (7)

In the device that extracts the feature from the input information and detects the abnormality by the input information,
A feature amount extraction unit for extracting features of the input video information and identification information;
A sensor cooperation unit that fuses the video information and the identification information;
A spatial information distribution apparatus comprising: a distribution unit that distributes feature quantities of the spatial information obtained by the fusion. A spatial information visualization unit that obtains the leveled visual information by leveling the type of spatial information with the level of detail and display content, and delivering the leveled visible information from the distribution unit. The spatial information distribution device according to 1. 2. The spatial information distribution apparatus according to claim 1, wherein the feature amount extraction unit fuses video information and identification information in a certain detection area and obtains the information every time. The spatial information distribution apparatus according to claim 2, wherein the spatial information visualization unit obtains video information and identification information in a certain detection area as multidimensional spatial data at predetermined time intervals. A cumulative database that accumulates past data is provided, and a feature amount analysis unit that issues a warning when the latest input information reaches a predetermined standard as compared with the information of the cumulative database is provided. Item 2. The spatial information distribution device according to item 1. The spatial information distribution apparatus according to claim 1, wherein a wireless tag is used as identification information, and the feature extraction unit identifies a target based on input information from a plurality of the tag information. 3. The spatial information distribution apparatus according to claim 2, wherein the distribution unit knows the level correspondence of the distribution destination and distributes predetermined visible information according to the level correspondence.

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