Intelligent security and protection method and early warning system based on behavior detection

Abstract

The invention relates to an intelligent security and protection method and an early warning system based on behavior detection, and the method comprises the steps: obtaining video data in a monitoring region through a camera; performing real-time analysis on the video data, and extracting human body behavior characteristics; performing abnormal behavior detection based on the behavior characteristics; when an abnormal behavior is detected, an early warning mechanism is triggered, and alarm information is sent; according to the invention, by analyzing the video data in the monitoring area, the human body behavior characteristics are extracted, and abnormal behavior detection is carried out based on the behavior characteristics, so that intelligent safety monitoring and early warning are realized.

Description

Technical Field

[0001] The present invention belongs to the field of smart security technology, and specifically relates to a smart security method and early warning system based on behavior detection. Background Art

[0002] In modern society, security issues are gaining increasing attention, prompting the emergence of smart security technologies. Traditional security systems rely primarily on manual monitoring, which can lead to blind spots and fatigue. Therefore, developing smart security methods and early warning systems that can automatically detect abnormal behavior and issue timely warnings is crucial. Summary of the Invention

[0003] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0004] In view of the following technical problems in the existing technology: traditional security systems mainly rely on manual monitoring, and there are problems such as monitoring blind spots and monitoring fatigue.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a smart security method based on behavior detection, comprising:

[0006] Obtain video data within the monitoring area through cameras;

[0007] Analyze video data in real time to extract human behavior characteristics;

[0008] Detect abnormal behavior based on behavioral characteristics;

[0009] When abnormal behavior is detected, the early warning mechanism is triggered and an alarm message is sent.

[0010] As a preferred technical solution for intelligent security methods and early warning systems based on behavior detection, it uses deep learning algorithms for human posture estimation and behavior recognition when analyzing video data in real time;

[0011] Based on the model trained by deep learning algorithm, the extracted human behavior features are classified and judged;

[0012] Determine whether there is abnormal behavior by comparing it with preset security rules and behavior models;

[0013] When abnormal behavior is detected, an alarm message is sent to security personnel or relevant departments.

[0014] As a preferred technical solution for intelligent security methods and early warning systems based on behavior detection, it combines video data from multiple cameras for comprehensive analysis during abnormal behavior detection to improve detection accuracy.

[0015] By setting up multiple sensors in the monitoring area, such as infrared sensors or sound sensors, more environmental information can be obtained to support behavior detection;

[0016] Use cloud computing platforms for data storage and processing to achieve large-scale intelligent security monitoring;

[0017] View monitoring images and receive early warning information in real time through mobile devices or remote monitoring terminals.

[0018] As a preferred technical solution for a smart security method and early warning system based on behavior detection, the classification and determination of the extracted human behavior characteristics includes:

[0019] Collect data sets containing human behavior and perform preprocessing;

[0020] Use deep learning models to extract features from preprocessed data;

[0021] Associate the extracted features with the corresponding human behavior categories and perform data annotation;

[0022] Use labeled datasets to train deep learning models and select appropriate hyperparameters;

[0023] The trained model is evaluated and tuned through evaluation indicators.

[0024] As an optimal technical solution for an intelligent security method and early warning system based on behavior detection, the model trained based on a deep learning algorithm includes multiple convolutional layers, pooling layers, and fully connected layers, which are used to extract human behavior features and classify them through a softmax function.

[0025] As an optimal technical solution for an intelligent security method and early warning system based on behavior detection, the deep learning model adopts a recurrent neural network structure to capture the temporal information of human behavior and perform classification and judgment.

[0026] As a preferred technical solution for intelligent security methods and early warning systems based on behavior detection, it also includes:

[0027] A data collection module, used to collect data including human behavior;

[0028] Data preprocessing module, which performs operations such as denoising, standardization, and alignment on the collected data;

[0029] Feature extraction module, which uses deep learning models to extract features from preprocessed data;

[0030] Data annotation module, which associates the extracted features with the corresponding human behavior categories;

[0031] The training module uses labeled datasets to train and tune deep learning models;

[0032] The classification and judgment module uses the trained model to classify and judge new human behavior features.

[0033] The present invention also discloses an early warning system based on the aforementioned behavior detection-based intelligent security method, including a camera and a computer, the camera is connected to the computer, the camera is also provided with a sensor, the sensor is connected to the computer, and the computer is provided with a communication module to communicate with the early warning center.

[0034] Beneficial effects of the present invention: The present invention can extract human behavior characteristics by analyzing video data in the monitoring area, and detect abnormal behavior based on the behavior characteristics, thereby realizing intelligent security monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0036] Figure 1 Schematic diagram of the early warning system of the present invention.

[0037] Reference numerals: camera 100 , computer 200 , communication module 300 , sensor 400 , early warning center 500 . DETAILED DESCRIPTION

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0040] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0041] Furthermore, the present invention is described in detail with reference to schematic diagrams. For ease of illustration, when describing the embodiments of the present invention, cross-sectional views illustrating device structures may be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of protection of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0042] Example 1

[0043] Reference Figure 1 This embodiment provides a smart security method based on behavior detection, including:

[0044] Obtain video data within the monitoring area through cameras;

[0045] Analyze video data in real time to extract human behavior characteristics;

[0046] Detect abnormal behavior based on behavioral characteristics;

[0047] When abnormal behavior is detected, the early warning mechanism is triggered and an alarm message is sent.

[0048] Use deep learning algorithms for human pose estimation and behavior recognition when analyzing video data in real time;

[0049] Based on the model trained by deep learning algorithm, the extracted human behavior features are classified and judged;

[0050] Determine whether there is abnormal behavior by comparing it with preset security rules and behavior models;

[0051] When abnormal behavior is detected, an alarm message is sent to security personnel or relevant departments.

[0052] During abnormal behavior detection, video data from multiple cameras is combined for comprehensive analysis to improve detection accuracy;

[0053] By setting up multiple sensors in the monitoring area, such as infrared sensors or sound sensors, more environmental information can be obtained to support behavior detection;

[0054] Use cloud computing platforms for data storage and processing to achieve large-scale intelligent security monitoring;

[0055] View monitoring images and receive early warning information in real time through mobile devices or remote monitoring terminals.

[0056] The classification and determination of the extracted human behavior characteristics include:

[0057] Collect data sets containing human behavior and perform preprocessing;

[0058] Use deep learning models to extract features from preprocessed data;

[0059] Associate the extracted features with the corresponding human behavior categories and perform data annotation;

[0060] Use labeled datasets to train deep learning models and select appropriate hyperparameters;

[0061] The trained model is evaluated and tuned through evaluation indicators.

[0062] The model trained based on the deep learning algorithm includes multiple convolutional layers, pooling layers and fully connected layers, which are used to extract human behavior features and perform classification through the softmax function.

[0063] The deep learning model adopts a recurrent neural network structure to capture the temporal information of human behavior and perform classification and judgment.

[0064] Also includes,

[0065] A data collection module, used to collect data including human behavior;

[0066] Data preprocessing module, which performs operations such as denoising, standardization, and alignment on the collected data;

[0067] Feature extraction module, which uses deep learning models to extract features from preprocessed data;

[0068] Data annotation module, which associates the extracted features with the corresponding human behavior categories;

[0069] The training module uses labeled datasets to train and tune deep learning models;

[0070] The classification and judgment module uses the trained model to classify and judge new human behavior features.

[0071] With reference to abnormal behaviors recorded and preset in advance, the system detects unusual movements through sensors and turns on the camera or obtains images, extracts human feature information from the images, and judges whether human movements are abnormal in chronological order. One type of abnormal behavior may be one or more of the following: raising the hand like holding a knife, running, or having a ferocious face. These behaviors are then classified to determine the degree of abnormality they fall into.

[0072] The warning level is then sent to the warning center alert.

[0073] Example 2

[0074] Reference Figure 1 This embodiment provides an early warning system based on the aforementioned behavior detection-based smart security method, including a camera 100 and a computer 200. The camera 100 is connected to the computer 200. The camera 100 is also provided with a sensor 400. The sensor 400 is connected to the computer 200. The computer 200 is provided with a communication module 300 for communicating with the early warning center 500.

[0075] In this embodiment, the camera 100 is used to acquire images, and a sensor 400 is provided next to the camera 100 for triggering monitoring. The sensor 400 may be an infrared sensor or a sound sensor. When there is an unusual sound or an object appears and moves, the camera 100 is triggered to monitor. If there is no unusual movement in the picture, monitoring may not be performed to save energy.

[0076] The computer 200 is used to analyze the acquired images, identify the human body in the images, and classify and determine the extracted human behavior characteristics. By comparing with the preset safety rules and behavior models, it is determined whether there is abnormal behavior. If there is an abnormality, the early warning center 500 is notified through the communication module 300 to issue an alarm. The alarm can be in the form of a telephone (if authorized), a text message (if authorized), or an alarm installed in the security center.

[0077] It will be appreciated that in the development of any actual embodiment, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will, for those of ordinary skill having the benefit of this disclosure, be a routine undertaking of design, fabrication, and production without undue experimentation.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A smart security method based on behavior detection, characterized by: include, Obtain video data within the monitoring area through cameras; Analyze video data in real time to extract human behavior characteristics; Detect abnormal behavior based on behavioral characteristics; When abnormal behavior is detected, the early warning mechanism is triggered and an alarm message is sent.

2. The intelligent security method based on behavior detection according to claim 1 is characterized in that: Use deep learning algorithms for human pose estimation and behavior recognition when analyzing video data in real time; Based on the model trained by deep learning algorithm, the extracted human behavior features are classified and judged; Determine whether there is abnormal behavior by comparing it with preset security rules and behavior models; When abnormal behavior is detected, an alarm message is sent to the early warning center.

3. The intelligent security method based on behavior detection according to claim 2, characterized in that: During abnormal behavior detection, video data from multiple cameras is combined for comprehensive analysis to improve detection accuracy; By setting up multiple sensors in the monitoring area, more environmental information can be obtained to support behavior detection; Use cloud computing platforms for data storage and processing to achieve large-scale intelligent security monitoring; View monitoring images and receive early warning information in real time through mobile devices or remote monitoring terminals.

4. The behavior-detection-based intelligent security method according to claim 3, characterized in that: The classification and determination of the extracted human behavior characteristics include: Collect data sets containing human behavior and perform preprocessing; Use deep learning models to extract features from preprocessed data; Associate the extracted features with the corresponding human behavior categories and perform data annotation; Use labeled datasets to train deep learning models and select appropriate hyperparameters; The trained model is evaluated and tuned through evaluation indicators.

5. The behavior-detection-based intelligent security method according to claim 4, characterized in that: The model trained based on the deep learning algorithm includes multiple convolutional layers, pooling layers and fully connected layers, which are used to extract human behavior features and perform classification through the softmax function.

6. The behavior-detection-based intelligent security method according to claim 5, characterized in that: The deep learning model adopts a recurrent neural network structure to capture the temporal information of human behavior and perform classification and judgment.

7. The intelligent security method based on behavior detection according to claim 6, characterized in that: Also includes, A data collection module, used to collect data including human behavior; Data preprocessing module, which performs operations such as denoising, standardization, and alignment on the collected data; Feature extraction module, which uses deep learning models to extract features from preprocessed data; Data annotation module, which associates the extracted features with the corresponding human behavior categories; The training module uses labeled datasets to train and tune deep learning models; The classification and judgment module uses the trained model to classify and judge new human behavior features.

8. An early warning system based on the intelligent security method based on behavior detection according to claims 1 to 7, characterized in that: include A camera (100) and a computer (200) are provided. The camera (100) is connected to the computer (200). The camera (100) is further provided with a sensor (400). The sensor (400) is connected to the computer (200). The computer (200) is provided with a communication module (300) for communicating with an early warning center (500).

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