JP5627962B2 - Anomaly detection device - Google Patents

Description

  The present invention relates to an abnormality detection apparatus using sound for detecting an abnormal state in a monitoring area based on acoustic information.

  Conventionally, an acoustic abnormality detection device has been proposed that detects abnormal sounds with abnormal waveforms such as burglary compulsive voices, gunshots, and screams of customers from acoustic signals acquired by the sound collection unit and reports them to security companies. (Patent Document 1). In addition, in order to reduce false detection due to ambient noise, an acoustic abnormality detection device that detects the presence of screams from ambient noise by analyzing the spectrum of the acoustic signal has also been proposed (Patent Document 2).

JP 5-6718 JP 9-251583 A

  However, the above-described conventional sound abnormality detection device detects an abnormal state in the monitoring area only from the sound signal, and thus erroneously detects an acoustic signal similar to an abnormal sound such as a surrounding announcement sound or television sound. There was a problem. In order to reduce such false detection, if the detection sensitivity is lowered, it may be a cause of detection failure. Therefore, it is difficult to adjust the detection sensitivity.

  By the way, as a result of analyzing the cases of many burglary cases, the inventor found that when burglary cases occurred, when abnormal sounds such as screams and gunshots occurred, at least two persons were located in the vicinity. noticed. That is, a sound similar to a scream that is crying alone for some reason is not a sound generated by a burglar case involving two or more persons, a bandit and a victim.

  Therefore, the present invention pays attention to the abnormal sound as described above and the number of people located in the vicinity, and even in an environment where various sounds similar to the abnormal sound exist, the abnormality in the monitoring area does not occur frequently. The purpose is to be able to detect the state.

  In order to solve such a problem, a sound collection unit that acquires an acoustic signal generated in a monitoring area in which an important object is stored, and the acoustic signal collected by the sound collection unit are analyzed to obtain a predetermined abnormality. An acoustic analysis unit that detects sound; an imaging unit that sequentially acquires an image of an area including the monitoring region; an image analysis unit that extracts a person image from the image and calculates the number of the person images; and the acoustic analysis An abnormality determination unit that determines an abnormal state in the monitoring area based on the abnormal sound detection result of the image analysis unit and the number of person images calculated by the image analysis unit, and an output unit that outputs the determination result of the abnormality determination unit An abnormality detection apparatus using sound, wherein the abnormality determination unit detects a plurality of the human images at the image analysis unit when the acoustic analysis unit determines that the sound is a predetermined abnormal sound. Anomaly detection characterized by determining an abnormal state when To provide a location.

With this configuration, the acoustic analysis unit of the present invention determines whether or not an abnormal sound such as a scream has occurred in the monitoring region by analyzing the acoustic signal collected by the sound collection unit. Further, the image analysis unit of the present invention analyzes an image of an area including at least the monitoring area acquired by the imaging unit, extracts a person image, and calculates the number of the person images. The abnormality determination unit of the present invention is in an abnormal state if a plurality of human images are detected by the image analysis unit when the acoustic analysis unit determines that an abnormal sound such as a scream has occurred in the monitoring area. Is determined. And the output part of this invention outputs the said determination result.
As a result, for example, when an employee is threatened by a burglar trying to take away an important item stored in a safe or the like, the acoustic analysis unit detects a scream uttered by the employee, and the image analysis unit Thus, by detecting a plurality of person images of a burglar and an employee, the abnormality determination unit can determine that the monitoring area is in an abnormal state. On the other hand, for example, even if a sound scream from a television set or the like installed in the monitoring area is detected by the acoustic analysis unit, if the image analysis unit cannot detect a plurality of human images, the abnormality determination unit is in the monitoring area. Is not determined to be abnormal. As described above, the present invention takes into consideration the indoor situation such as whether or not there are a plurality of persons even if the monitoring area is an environment where various sounds similar to abnormal sounds exist. The frequent occurrence of false detection can be suppressed.

  As a preferred aspect of the present invention, the abnormal sound detected by the acoustic analysis unit is a human screaming voice.

  As described above, the anomaly detection device of the present invention is prone to erroneous detection even in an environment where various sounds similar to the anomalous sound exist by actively using the location information of the person in the monitoring area. An abnormal state in the monitoring area can be detected without doing so.

Diagram showing configuration and layout image of anomaly detection device Block diagram showing the configuration of the management device Diagram showing movement trajectory information Flow chart showing processing in the control unit Flow chart showing multiple person image detection processing Flow chart showing scream detection processing Flow chart showing abnormality determination processing

  Hereinafter, as an embodiment of the present invention, a room in a building where a safe in a building is set as a monitoring area, and acoustic information in the monitoring area and image information obtained by imaging a predetermined area in the monitoring area are used. An embodiment for detecting an abnormal state will be described with reference to the drawings.

FIG. 1 is a diagram schematically showing the overall configuration and arrangement of the abnormality detection device 1. The abnormality detection device 1 includes a management device 2, an imaging device 3, and a sound collection device 4.
In this embodiment, the abnormality detection apparatus 1 uses the room in which the safe 7 is installed as a monitoring area, and when a scream, which is an abnormal sound generated in the monitoring area, is detected, the monitoring image from the imaging apparatus 3 is detected. When a plurality of person images are detected, the abnormal state is determined, and a process of notifying the security center device 6 located at a remote location via the security device 5 is performed.

  The management device 2 is installed in an office or a security room (not shown) in the building, and is based on the monitoring image transmitted from the imaging device 3 connected to the management device 2 and the acoustic signal transmitted from the sound collection device 4. An abnormal state detection process is performed. Moreover, the management apparatus 2 alert | reports an abnormal condition by outputting a warning by an audio | voice or an image display to the administrator, guard, etc. who exist in an office etc. at the time of the detection of an abnormal condition. Further, the management device 2 is connected to the security device 5 and transmits an abnormal signal to the security device 5 when an abnormal state is detected.

  The image pickup apparatus 3 is a so-called surveillance camera that includes an image pickup element such as a CCD element or a C-MOS element, optical system components, and the like. The imaging device 3 is installed on the upper part or ceiling of the indoor wall 9 and is installed so as to capture an image of the monitoring area from above. Preferably, the imaging device 3 is installed so that the imaging range is substantially the entire room (closed space). The imaging device 3 captures the monitoring area at predetermined time intervals and sequentially transmits monitoring images to the management device 2. The time interval at which the input image is captured is 1/5 second, for example.

  The sound collecting device 4 is configured to include a microphone, an amplifier, an A / D converter, and the like, and is an electric circuit that converts sound in a monitoring space into a digital signal (acoustic signal). The sound collection device 4 is installed at a position where sound generated at least in a region including the monitoring region can be collected, converts the sound into an acoustic signal, and outputs the sound signal to the management device 2. Note that the amplifier is set in advance so that the volume of the sound signal output when a scream is generated in the room where the safe 7 is installed falls within the range of 70 to 100 dB.

  The security device 5 connected to the management device 2 is installed in a not-shown office room, security room or the like in the same building as the monitoring area. When the security device 5 receives the abnormality signal from the management device 2, the security device 5 transmits the abnormality to the security center device 6 via a wide area communication network such as a public telephone line, and the security device resident in a security center (not shown) at a remote location. Inform the employee of the occurrence of an abnormality.

  FIG. 2 shows the configuration of the management device 2. The management device 2 has a computer function and includes a storage unit 21, a control unit 22, an input unit 23, an output unit 24, and a communication unit 25.

  The communication unit 25 is a communication interface such as a LAN or USB, and communicates with the imaging device 3, the sound collection device 4, and the security device 5.

  The input unit 23 is an information input device such as a keyboard, a mouse, a touch panel, or a portable storage medium reading device. An administrator or the like can input various setting information such as an amplification factor, operation information, and the like to the management apparatus 2 using the input unit 23.

The output unit 24 is an interface for outputting processing results from the control unit 22 to various external devices. The output unit 24 is connected to a sound output device such as a speaker or a buzzer, for example, and outputs an abnormal signal that causes the sound output device to sound a warning sound according to an instruction from the control unit 22. The output unit 24 is connected to a display output device such as a display, for example, and causes the display output device to display and output a warning message according to an instruction from the control unit 22. An administrator or the like can detect an abnormal state in the monitoring area by confirming the display output or the audio output from the output unit 24 as a warning output. Note that the administrator or the like can also confirm the setting information of the abnormality detection device 1, the monitoring image in the monitoring area, and the like by confirming the display output.
The output unit 24 may include a communication interface that transmits an abnormal signal to the external security device 5. Thereby, when an abnormal state is detected by the process of the control unit 22, the management apparatus 2 can notify a signal indicating the abnormal state to an external monitoring center or the like. In this case, the output unit 24 may be an interface device common to the communication unit 25.

  The storage unit 21 is an information storage device such as a ROM, RAM, or HDD. The storage unit 21 stores various programs and various data, and inputs / outputs such information to / from the control unit 22. Various data includes a vowel feature quantity 211 and movement trajectory information 212.

The vowel feature quantity 211 is vowel feature quantity data created in advance, and is referred to in order to extract a vowel from the acoustic signal in the vowel extraction means 224 described later. The vowel feature quantity 211 is stored in association with an identifier representing each vowel type for each vowel type.
The original data of the vowel feature quantity 211 is a sample acoustic signal at the end of a scream collected from many speakers. In advance, a spectral envelope parameter representing the frequency feature amount of the scream ending is extracted from each of these sample acoustic signals, and the distribution of the parameter is learned for each type of vowel. In this embodiment, an 8th-order LPC cepstrum (LPC: Linear Predictive Coding) is used as a parameter representing a spectral envelope, and a GMM (Gaussian Mixture Model) is used as a distribution. The frequency feature quantity is not limited to this, and various spectrum parameters known in speech analysis such as MFCC (Mel-Frequency Cepstral Coefficients) can also be used. In addition, three types of “a”, “e”, and “o” are used as vowels at the end of the scream. That is, as the vowel feature quantity 211, the GMM of the “PC” LPC cepstrum, the GMM of the “PC” LPC cepstrum, and the GMM of the “PC” LPC cepstrum are stored in the storage unit 21, respectively.

  The movement trajectory information 212 analyzes the monitoring image acquired by the imaging apparatus 3 and tracks the movement trajectory of the person image by storing the coordinates of the person image included in the monitoring image on the monitoring image in time series. Information generated so that it can be used. As a specific example of the movement trajectory information 212, as shown in FIG. 3, the time when the monitoring image is acquired and a unique image given to the person image when the person image is included in the monitoring image. It is stored in the storage unit 21 as a table that associates the image ID that is the identifier and the coordinates of the center of gravity of the person image on the monitoring image. As will be described later, the image ID is analyzed by the control unit 22 between the person image in the monitoring image at the past time and the person image at the current time, and the person image at the past time and the person image at the current time are analyzed. Are determined to be the same, a common image ID is assigned, and if it is determined that the person image at the past time and the person image at the current time are not the same, a new image ID is assigned.

  The control unit 22 is an arithmetic device such as a CPU or a DSP, for example, and executes various types of information processing according to programs stored in the storage unit 21. In the present embodiment, the control unit 22 analyzes the acoustic signal acquired by the sound collecting device 4 and the monitoring image acquired by the imaging device 3, and outputs a signal corresponding to the content of the abnormality when an abnormal state is detected. An abnormality detection process to be output to the output unit 24 is performed. In addition, the control unit 22 performs processing for storing input information such as setting information and operation information from the input unit 23 in the storage unit 21. The control unit 22 functionally includes a person image extraction unit 221, a tracking unit 222, a person image calculation unit 223, a vowel extraction unit 224, a scream determination unit 225, and an abnormality determination unit 226. The

  The person image extraction unit 221 performs person image extraction processing for extracting one or a plurality of person images from the monitoring images for each of the monitoring images captured every predetermined time by the imaging device 3. Specifically, for example, the person image extraction unit 221 stores a monitoring image obtained by imaging a monitoring space in a state where no one is present in advance in the storage unit 21 as a background image, and the background image and the monitoring image are recorded. A difference pixel is extracted by comparing with an image. Then, among the difference pixel group configured to include the difference pixels adjacent to each other, a difference pixel group having a shape satisfying a predetermined condition and a size greater than or equal to a predetermined value is extracted as a human image representing a person. In addition to the above method, the person image extraction unit 221 may extract a person image by combining various image processes such as edge detection. Information relating to the person image extracted by the person image extraction means 221 (for example, the amount of the image feature such as the position of the person image and the color histogram) is stored in the storage unit 21 and is used for tracking the person image by the tracking means 222 described later. Used.

  The tracking unit 222 performs tracking processing of a person image each time the person image extraction unit 221 extracts a person image for each monitoring image acquired by the imaging device 3 at predetermined time intervals. In the person image tracking process, the tracking unit 222 extracts each person image extracted by the person image extracting unit 221 from the newly captured monitoring image at the past time and stored in the storage unit 21. And the movement path of the person corresponding to the person image in the monitoring space is stored in the movement locus information 212 of the storage unit 21. Specifically, the tracking unit 222 captures information related to each person image extracted from the new monitoring image (for example, the amount related to the position of the person image and the image feature) and is stored in the storage unit 21 last time. The person images representing the same person are associated with each other by comparing with the information about the person image extracted from. Then, the same image ID is assigned to the person images associated with each other as the person image representing the same person, and the coordinates on each monitoring image captured in time series are associated with the image ID and the movement locus of the storage unit 21. Saved in information 212.

The person image calculation means 223 performs a person image calculation process for calculating the total number of person images extracted at the current time. In this embodiment, the human image calculation process is performed by referring to the movement trajectory information 212 and obtaining the total number of human image IDs tracked at the current time.
In addition, the person image calculation unit 223 performs a process of turning on the multiple person flag in the storage unit 21 when the total number of calculated human images is plural (two or more). Here, the multi-person flag is a flag indicating that a plurality of person images are detected.

  The vowel extraction means 224 extracts the vowel part of the sound from the acoustic signal collected by the sound collection device 4 and outputs the extraction result to the scream determination means 225. That is, the vowel extraction unit 224 calculates the frequency feature amount of the input acoustic signal for each frame, compares each frequency feature amount with the vowel feature amount 211, and determines whether each frame has the frequency feature amount of the vowel. The determination result is output to the scream determination means 225.

  Preferably, the vowel extraction unit 224 also sends information on the vowel type (“A”, “E”, or “O”) of the frame to the scream determination unit 225 when a frame having the frequency characteristic amount of the vowel is determined. Output. The vowel “A”, “E” or “O” extracted by the vowel extraction means 224 is a vowel typically uttered as a scream ending. Examples of typical screams that end with "A" include "Kya", "Gya", "Wow", "Wow", and "Awow". Typical examples of screams that end with "E" For example, you can list "help me", "stop me", "help me", "stop me" as an example of a typical scream that ends with "o" Can be mentioned.

  The frequency feature amount calculated by the vowel extraction unit 224 is the same type as the vowel feature amount 211, and is an eighth-order LPC cepstrum in this example. Values suitable for speech analysis are set in advance for the frame length and frame period. In this example, the frame length is 20 ms and the frame period is 10 ms. Specifically, the vowel extraction unit 224 calculates the distance between the frequency feature amount of each frame of the input acoustic signal and the vowel feature amount 211 of each vowel, and compares it with a preset vowel determination threshold value. If the distance between the frequency feature quantity and one of the vowel feature quantities 211 is equal to or smaller than the vowel determination threshold value, the frame is determined to be a vowel and a vowel type corresponding to the vowel feature quantity 211. On the other hand, the vowel extraction unit 224 determines that a frame having no vowel feature quantity 211 whose distance is equal to or smaller than the vowel determination threshold is not a vowel.

但し、Ｋは距離算出対象の母音特徴量２１１を構成している正規分布の数、ｋはそのインデックス番号（ｋ＝１，…，Ｋ）を表しており、ｎは周波数特徴量の次数、ｉはそのインデックス番号（ｉ＝１，…，ｎ）を表している。また、ｘ_ｉは入力音響信号の周波数特徴量を表すベクトルのｉ番目要素、ｍ_ｋ，ｉはｋ番目の正規分布の平均を表す平均ベクトルのｉ番目要素、σ^２ _ｋ，ｉはｋ番目の正規分布におけるベクトルｉ番目要素の分散、ｗ_ｋはｋ番目の正規分布の重み係数、をそれぞれ表している。 In this example in which the vowel feature quantity 211 is stored in the GMM, the distance D between each vowel feature quantity 211 and the frequency feature quantity of the frame is calculated by Equation 1.

Here, K is the number of normal distributions constituting the vowel feature quantity 211 for distance calculation, k is the index number (k = 1,..., K), n is the order of the frequency feature quantity, i Represents the index number (i = 1,..., N). X _i is the i-th element of the vector representing the frequency feature quantity of the input acoustic signal, m _{k, i} is the i-th element of the average vector representing the average of the k-th normal distribution, and σ ² _{k, i} is the k-th element. The variance of the vector i-th element in the normal distribution, w _k represents the weight coefficient of the k-th normal distribution, respectively.

  The scream determining unit 225 determines whether or not the processing target frame has a volume (power) equal to or higher than a preset scream volume threshold, thereby determining whether or not the volume is a scream volume. Perform the process. Moreover, the process which detects a scream is performed by determining whether the vowel part extracted by the vowel extraction means 224 continues more than preset scream determination time. Note that values suitable for detection of the scream ending are preset in the scream volume threshold and the scream determination time. In this example, the scream volume threshold is 70 dB, and the scream determination time is 200 ms. Incidentally, in this example in which the frame period is set to 10 ms, the scream determination time converted into the number of frames is 20 frames.

  As described above, the utterance contents of the entire screaming voice are various, but the utterance contents to be extracted can be reduced to at most three types by paying attention to the ending of the screaming. As a result, the trouble that the utterance content fails to detect an unexpected scream is significantly reduced. Also, by having a vowel part, it reduces the problems of falsely detecting screams other than screams with high volume and long duration, such as coughing, sneezing, horning, closing sounds, squeaking sounds, falling sounds of cans, etc. be able to. In addition, it is possible to reduce the problem of erroneously detecting a normal volume conversation voice as a scream according to the volume condition of the vowel part. Further, it is possible to reduce the problem of erroneously detecting a loud vowel such as a laughing voice as a scream according to the condition of the duration time of the vowel part.

  Here, if a section in which a vowel is continuous is included in a word of loud conversational speech, there is a possibility of erroneous detection as a scream. Therefore, the scream determination means 225 refers to the vowel type extracted by the vowel extraction means 224 to determine whether each section in which the same vowel continues is the scream part (determination section) or not. That is, it is determined whether sections having different vowel types are screamed as different vowel parts. Thus, for example, even if a section of “Ao” in which “A” and “O” are consecutive is included in a word of a loud conversation voice, and the length of the section of “AO” reaches the scream determination time, Since the “a” section and the “o” section are separate determination sections, it is possible to reduce the problem of erroneously detecting this loud conversation voice as a scream.

  The abnormality determination unit 226 is configured to detect a plurality of person images when a scream is detected based on the multiple person flag that is the detection result of the human image calculation unit 223 and the scream flag that is the scream detection processing result of the scream determination unit 225. Is detected, and an abnormal state determination process in the monitoring area is performed. If it is determined that there is an abnormal condition within the monitoring area, the abnormality determination unit 226 causes the output unit 24 to output a warning.

Hereinafter, an example of the flow of processing executed by the control unit 22 of the management device 2 according to the present embodiment will be described based on the flowcharts of FIGS.
FIG. 4 is a flowchart showing an abnormality detection process in the control unit 22.

  Prior to the operation, various initial settings such as setting of a scream volume threshold are performed by an administrator or the like using the input unit 23 of the management device 2 (S1). In the initial setting, first, when an administrator who has confirmed that a room is unattended starts up the management apparatus 2, the person image extraction unit 221 of the control unit 22 uses a monitoring image captured at a predetermined time after the startup. A background image is generated and stored in the storage unit 21. In addition, in the initial setting, a scream volume threshold value, a scream determination time, and the like are input and registered as necessary in addition to the above. When the initial setting is completed, the abnormality detection process in steps S3 to S8 is repeated each time a new monitoring image is input from the imaging device 3.

Next, returning to FIG. 4, the multiple person image detection process in step S3 will be described. The multiple person image detection process is a process performed by the person image extraction unit 221, the tracking unit 222, and the person image calculation unit 223 every time a monitoring image is acquired by the imaging device 3 (that is, every frame period of the monitoring image). is there.
FIG. 5 is a flowchart illustrating the multiple person image detection process according to the present embodiment. Below, a multiple person image detection process is demonstrated based on the flowchart of FIG.

  As shown in FIG. 5, first, when a monitoring image at the current time is acquired from the imaging device 3 (S30) and the monitoring image is transmitted via the communication unit 25, the person image extraction unit 221 displays the monitoring image. And the background image are compared, and a human image extraction process is performed (S32).

  Next, the control unit 22 determines whether or not one or more person images have been extracted in the person image extraction process of step S32 (S34). When the person image is not extracted (S34-No), the monitoring image acquired in step S30 is stored in the storage unit 21 as a new background image (S44), and the process proceeds to step S46. Here, the reason why the background image is updated when the person image is not extracted is to cope with the case where the background image changes with time.

  When a person image is extracted in the person image extraction process in step S32 (S34-Yes), the tracking unit 222 performs the person image tracking process described above (S36). Next, the person image calculation means 223 performs a person image calculation process (S38).

  Next, the person image calculation means 223 determines whether or not there are a plurality of person images (S40). If there are a plurality of person images (S40-Yes), the person image calculating unit 223 turns on the plurality of person flags (S42). The multiple person image detection process ends, and the process proceeds to step S5.

  On the other hand, when a plurality of person images do not exist in step S40 (S40-No), or when the background image is updated in step S44, the plurality of person flags are turned off (S46), and the plurality of person image detection processes are terminated. The process proceeds to step S5.

Subsequently, returning to FIG. 4, the scream detection process in step S <b> 5 will be described. The scream detection processing is performed by the vowel extraction unit 224 and the scream determination unit 225, and is performed for each predetermined frame period for the acoustic signal transmitted by the sound collection device 4 and stored in the storage unit 21. Process. That is, the control unit 22 confirms whether or not an acoustic signal having a frame period length is newly stored in the storage unit 21. When the frame period arrives, the control unit 22 reads out frame data of a preset frame length from the storage unit 21, performs windowing processing using a Hamming window function, and screams the windowed frame data. Perform detection processing.
FIG. 6 is a flowchart illustrating the scream detection process according to the present embodiment. Below, a scream detection process is demonstrated based on the flowchart of FIG.

  When the frame data is read from the storage unit 21 by the control unit 22, the scream determination unit 225 calculates the volume (power) of the frame data (S52) and compares the calculated volume with a scream volume threshold ( S54). If the volume does not exceed the scream volume threshold (S54-No), the scream determination means 225 determines that there is no scream, the control unit 22 resets the value of the scream counter (S70), and ends the scream detection process. Then, the process proceeds to step S8. On the other hand, if the volume exceeds the scream volume threshold (S54-Yes), the scream determination unit 225 proceeds with the process to step S56 because there is a possibility that a scream has occurred.

  Next, the vowel extraction unit 224 calculates the frequency feature amount of the frame data (S56). Then, the vowel extraction unit 224 sequentially reads out the vowel feature quantity 211 of “A”, the vowel feature quantity 211 of “e”, and the vowel feature quantity 211 of “O” from the storage unit 21, and each read vowel feature quantity 211 and It is determined whether the frame data is a vowel by comparing the frequency feature amount of the frame data (S56). In addition, the vowel extraction unit 224 specifies the vowel type when the frame data is determined to be a vowel.

  When it is determined that the frame data is not a vowel (S58-No), the vowel extraction unit 224 outputs to the scream determination unit 225 that a vowel has not been extracted. Upon receiving this output, the scream determining means 225 determines that there is no scream. And the control part 22 resets the value of a scream counter (S70), complete | finishes a scream detection process, and advances a process to step S8.

  On the other hand, if it is determined that the frame data is a vowel (S58-Yes), the vowel extraction unit 224 checks whether or not the specified vowel type is the same as the previously specified vowel type and is specified this time. The vowel type is stored in the storage unit 21 (S60). In the next confirmation, the current vowel type stored in the storage unit 21 is referred to as the previous vowel type.

If the vowel type specified this time is the same as the vowel type specified last time (S60-Yes), the vowel extraction unit 224 outputs to the scream determination unit 225 that the vowel is continuously extracted. Upon receiving this output, the scream determining means 225 increments the scream counter by 1 (S62).
On the other hand, if the vowel type specified this time is not the same as the vowel type specified last time (S60-No), the vowel extraction unit 224 outputs to the scream determination unit 225 that a new vowel has been extracted. Upon receiving this output, the scream determining means 225 sets 1 to the scream counter (S64).
When the value of the scream counter is updated in step S62 or step S64 in this way, the scream determination means 225 compares the scream counter with the scream determination time (S66). If the scream counter does not exceed the scream determination time (S66-No), it is determined that the scream determination unit 225 is continuing the determination, and the control unit 22 ends the scream detection process until the next frame period arrives, step S8. Proceed to the process.

  When the scream counter has exceeded the scream determination time (S66-Yes), the scream determination means 225 turns on the scream flag (S68). Then, the control part 22 resets the value of a scream counter (S70), complete | finishes a scream detection process, and advances a process to step S8.

Next, returning to FIG. 4, the abnormality determination process in step S8 will be described. The abnormality determination process in step S8 is a process performed by the abnormality determination unit 226 based on the multiple person flag and the scream flag in the storage unit 21.
FIG. 7 is a flowchart illustrating the abnormality determination process according to the present embodiment. Hereinafter, the abnormality determination process will be described based on the flowchart of FIG.

  As shown in FIG. 7, first, the abnormality determination unit 226 determines whether or not the scream flag in the storage unit 21 is ON (S80).

  When the scream flag is ON in step S80 (S80-Yes), the abnormality determination unit 226 determines whether the multiple person flag in the storage unit 21 is ON (S82).

If the multi-person flag is ON in step S82 (S82-Yes), the abnormality determination unit 226 outputs a warning signal to the output unit 24. When the output unit 24 receives a warning signal from the abnormality determination unit 226 of the control unit 22, the output unit 24 performs a warning output process (S84).
In the warning output process, the output unit 24 performs a predetermined warning output. For example, a warning sound corresponding to the type of abnormal state is sounded from a speaker connected to the output unit 24, and the type of abnormal state is output to the display connected to the output unit 24 as a warning message. An administrator or the like can detect an abnormal state in the monitoring area by checking these warning sounds and warning messages. Further, the output unit 24 notifies the security device 5 of the abnormal state via the communication unit 25. Thereby, the management apparatus 2 can notify the abnormal state to the security center apparatus 6 via the security apparatus 5, and can notify the abnormality to the security staff who is resident and monitored at the external monitoring center. .

  When the multi-person flag is not ON in step S82 (S82-No), or when an alarm output process is performed in step S84, the abnormality determination unit 226 performs a process of turning off the scream flag (S86).

  If the scream flag is turned OFF in step S86, or if the scream flag is not ON in step S80 (S80-No), the abnormality determination process is terminated, and the process returns to step S3. The process of step S8 is repeated.

  The present invention is not limited to the above embodiments, and may be implemented in various different embodiments within the scope of the technical idea described in the claims. Moreover, the effect described in the Example is not limited to this.

  In the above-described embodiment, the person image calculation unit 223 refers to the movement trajectory information 212 that is the result of the tracking process by the tracking unit 222 and obtains the total number of image IDs of the person images tracked at the current time. An image calculation process is performed. However, the present invention is not limited to this, and the person image calculation unit 223 may obtain the total number of person images by performing a process of counting the number of person images extracted by the person image extraction unit 221. That is, even when the tracking unit 222 is omitted and the tracking process is not performed, the effect of the present invention can be obtained.

In the said Example, the abnormal state in the monitoring area | region is detected by detecting a scream as an abnormal sound. However, the present invention is not limited to this, and an abnormal state in the monitoring area may be detected by detecting an abnormal sound other than a scream, for example, an abnormal sound such as a gunshot or a roar. For example, when a sound volume equal to or higher than a preset abnormal sound volume threshold is detected, it can be simply regarded as an abnormal sound such as a gunshot or anger. That is, after calculating the sound volume in step S52, it can be realized by comparing with the abnormal sound volume threshold value and turning on the scream flag when the sound volume exceeds the abnormal sound volume threshold value.
Further, the destructive sound generally has a remarkable feature in the rise and fall of the detected sound, such that the loudness is generally detected rapidly and then attenuated and reduced in a short time. Therefore, when a destructive sound is detected as an abnormal sound, the sound rise rate, peak volume, fall decay rate, and duration of the sound signal acquired by the sound collector 4 are calculated, and these values are set in advance. It is possible to determine whether or not the sound is a destructive sound by collating with a threshold having a destructive sound characteristic. In addition, by analyzing the frequency component of the sound that is emitted when a safe or the like is struck in advance, whether or not there is a specific frequency component in the acoustic signal at the time of detection, It is possible to greatly improve the certainty of determination. In addition, since it is difficult to destroy a strong box such as a safe, it is detected as an abnormal sound when a similar destructive sound is observed multiple times in a short time. It is also effective to impose conditions such as determining

  The embodiment of the present invention has been described above. In the present embodiment, the abnormality detection device 1 functions as the abnormality detection device of the present invention. The sound collecting device 4 functions as the sound collecting unit of the present invention. Further, the vowel extraction means 224 and the scream determination means 225 of the control unit 22 function as an acoustic analysis unit of the present invention. The imaging device 3 functions as an imaging unit of the present invention. In addition, the person image extraction unit 221, the tracking unit 222, and the person image calculation unit 223 of the control unit 22 function as the image analysis unit of the present invention. The output unit 24 functions as the output unit of the present invention.

DESCRIPTION OF SYMBOLS 1 ... Abnormality detection apparatus 2 ... Management apparatus 3 ... Imaging apparatus 4 ... Sound collector 5 ... Security apparatus 6 ... Security center apparatus 7 ... Safe 9 ... Wall 21 ... Storage unit 22 ... Control unit 23 ... Input unit 24 ... Output unit 25 ... Communication unit 211 ... Vowel feature 212 ... Movement trajectory information 221 ... Human image extraction Means 222 ... Tracking means 223 ... Human image calculation means 224 ... Vowel extraction means 225 ... Scream judgment means 226 ... Abnormality judgment means

Claims (2)

A sound collection unit for acquiring an acoustic signal generated in the monitoring area;
Analyzing the acoustic signal collected by the sound collecting unit, and detecting a predetermined abnormal sound; and
An imaging unit that sequentially acquires an image of an area including the monitoring area;
An image analysis unit that extracts a person image from the image and calculates the number of the person images;
An abnormality determination unit that determines an abnormal state indicating occurrence of a burglary case in the monitoring area based on the abnormal sound detection result of the acoustic analysis unit and the number of human images calculated by the image analysis unit;
An abnormality detection device by sound composed of an output unit that outputs a determination result of the abnormality determination unit,
The abnormality determining unit determines an abnormal state when a plurality of the person images are detected by the image analyzing unit when the acoustic analyzing unit determines that the sound is a predetermined abnormal sound. Detection device. The abnormality detection device according to claim 1, wherein the abnormal sound detected by the acoustic analysis unit is a human screaming voice.

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