Crowd monitoring and security system

Abstract

A crowd control and security surveillance system comprising: an unmanned aerial vehicle (UAV) equipped with a variety of sensors and configured to collect real-time aerial data when deployed in an environment; a real-time data analysis module in conjunction with the UAV, comprising a machine learning-based processor for processing the data collected by the integrated sensor to detect anomalies, wherein the real-time data analysis module uses machine learning protocols to detect anomalies; an autonomous decision-making module in communication with the real-time data analysis module, configured to determine crowd behavior and analyze potential hazards, the autonomous decision-making module comprising a processor that determines the best course of action with respect to detected anomalies and crowd behavior based on commands received from the real-time data analysis module; a microcontroller operatively coupled to the UAV and its sensors, a real-time data analysis module, and an autonomous decision-making module, the microcontroller configured to operate a display unit and an audio unit configured to inform the crowd about the dynamic path planning and generate emergency alerts; a communication module connected to the microcontroller and configured to enable the UAV to communicate with ground control stations, other UAVs, and directly with the crowd via the display unit and the audio unit to send messages or use sound or light signals; and an integrated security system operatively connected to the autonomous decision module, the microcontroller, and the communications module and configured to coordinate response actions upon detection of a threat, wherein predefined security protocols stored in a memory are activated by the integrated security system when the autonomous decision module detects a threat.

Description

FIELD OF THE INVENTION

[0001] The present disclosure relates to a surveillance system, in particular for crowd monitoring and security. The invention particularly relates to a surveillance system using an unmanned aerial vehicle (UAV) with advanced sensors and real-time data analysis capabilities for crowd monitoring and security. The proposed system acquires aerial data via multiple sensors and subsequently analyzes it using machine learning-based modules. The UAV in the system is configured to detect anomalies and initiate appropriate emergency measures to minimize damage and ensure public safety. BACKGROUND OF THE INVENTION

[0002] Unmanned aerial vehicles (UAVs), or drones, are currently used for a variety of applications, from video recording to door-to-door delivery. However, UAVs are not currently used for crowd control or security systems.

[0003] Currently, there is no UAV configured for crowd monitoring and triggering a security system, where such a UAV is equipped with multiple sensors such as LiDAR, infrared cameras, ultrasonic sensors, microphone arrays, thermal imaging cameras, environmental sensors, GPS, and machine learning-based sensors.

[0004] The above-mentioned UAV-based crowd security and surveillance system can potentially be used in situations such as trade fairs, concerts, and emergency evacuations.

[0005] In view of the previous discussion, it is clear that there is a need for a UAV-based surveillance system for crowd monitoring and security. Summary of the invention

[0006] The present disclosure relates to a surveillance system for crowd monitoring and security. The proposed system utilizes an unmanned aerial vehicle (UAV) to monitor public areas. The UAV is equipped with lidar, infrared cameras, ultrasonic sensors, a microphone array, thermal imaging cameras, environmental sensors, and GPS, and collects real-time aerial data. The system further includes a real-time data analysis module for data processing to detect anomalies. This module is connected to an autonomous decision-making protocol to analyze crowd behavior and predict potential hazards. The system further includes a controller coupled to the module, the sensors, and the UAV, which controls a display and an audio unit to direct the crowd for dynamic route planning and emergency calls.The controller is connected to a communications module that allows the UAV to communicate with ground control stations and other UAVs, or even directly with the crowd via the display and audio unit for message transmission. The system also includes an integrated security system connected to the decision module to ensure coordinated response measures to detected threats, thus increasing public safety in crowded areas.

[0007] The present disclosure aims to provide a surveillance system for crowd monitoring and security. The system comprises: an unmanned aerial vehicle (UAV) with multiple integrated sensors that collects real-time aerial data when deployed in an environment; a real-time data analysis module connected to the UAV, including a machine learning-based processor, for processing the data collected by the integrated sensor to detect anomalies, the real-time data analysis module utilizing machine learning protocols for anomaly detection; an autonomous decision-making module connected to the real-time data analysis module that determines crowd behavior and analyzes potential hazards, the module including a processor that determines the best course of action regarding detected anomalies and crowd behavior based on commands received from the real-time data analysis module;A microcontroller operatively coupled to the UAV and its sensors, the real-time data analysis module, and the autonomous decision-making module. The microcontroller controls a display unit and an audio unit that guides the crowd in dynamic path planning and generates emergency alerts; a communication module operatively connected to the controller and configured to enable the UAV to communicate with ground control stations, other UAVs, and directly with the crowd via the display unit and audio unit to send messages or use sound or light signals.and an integrated security system operatively connected to the autonomous decision module, the microcontroller, and the communications module and configured to coordinate response actions upon detection of a threat, wherein predefined security protocols stored in a memory are activated by the integrated security system when the autonomous decision module detects a threat.;

[0008] An object of the present disclosure is to provide a surveillance system for crowd monitoring and security.

[0009] Another objective of the present disclosure is to develop a system that can detect vandalism to deter crowds.

[0010] Another object of the present disclosure is to provide a surveillance system capable of directing the crowd to take a different travel route by detecting conspicuous events in the vicinity of the crowd.

[0011] Another object of the present disclosure is to develop a system that can communicate with other surveillance systems to control crowd movement based on real-time data.

[0012] To further clarify the advantages and features of the present disclosure, the invention will be explained in more detail with reference to specific embodiments illustrated in the accompanying drawings. These drawings illustrate only typical embodiments of the invention and are therefore not to be considered as limiting its scope. The invention will be described and explained in more detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE CHARACTERS

[0013] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. Fig. 1 shows a block diagram of a crowd monitoring and security surveillance system according to an embodiment of the present disclosure. Fig. 2 shows a diagram of the unmanned aerial vehicle of the proposed system according to an embodiment of the present disclosure.

[0014] Those skilled in the art will also appreciate that the elements in the drawings are shown for convenience and are not necessarily to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may be represented in the drawings by conventional symbols. The drawings may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art from the present description. DETAILED DESCRIPTION:

[0015] To facilitate understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and will be clearly described. However, the scope of the invention is not limited thereby. Changes and further modifications to the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to one skilled in the art to which the invention pertains.

[0016] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0017] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, the language "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not refer to the same embodiment.

[0018] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps may include not only those steps, but also additional steps not expressly listed or inherent in that process or method. Likewise, the statement "comprises" for one or more devices, subsystems, elements, structures, or components does not exclude, without further limitation, the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. The systems, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0020] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0021] Fig. 1 shows a block diagram of a crowd monitoring and security surveillance system (100) according to an embodiment of the present disclosure.

[0022] According to Fig. 1, the system (100) comprises: an unmanned aerial vehicle (UAV) (102) having a plurality of integrated sensors (102a) configured to collect real-time aerial data when deployed in an environment; a real-time data analysis module (104) connected to the UAV (102) and comprising a machine learning-based processor (104a) for processing the data collected by the integrated sensor and detecting anomalies, the real-time data analysis module (104) using machine learning protocols to detect anomalies;an autonomous decision-making module (106) connected to the real-time data analysis module (104) and configured to determine crowd behavior and analyze potential hazards, the autonomous decision-making module (106) comprising a processor that determines the best course of action with respect to detected anomalies and crowd behavior based on commands received from the real-time data analysis module; a microcontroller (108) operatively coupled to the UAV (102) and its sensors (102a), the real-time data analysis module (104), and the autonomous decision-making module (106), the microcontroller (108) configured to control a display unit (108a) and an audio unit (108b) configured to guide the crowd in dynamic path planning and to generate emergency alerts;a communications module (110) operatively connected to the microcontroller (108) and configured to enable the UAV (102) to communicate with ground control stations, other UAVs, and directly with the crowd via the display unit (108a) and audio unit (108b) to send messages or use sound or light signals; and an integrated security system (112) operatively connected to the autonomous decision module (106), the microcontroller (108), and the communications module (110) and configured to coordinate response measures upon detection of a threat, wherein predefined security protocols stored in a memory are activated by the integrated security system (112) when the autonomous decision module (106) detects a threat.

[0023] In one embodiment, the plurality of sensors (102a) of the UAV (102) includes sensors such as ultrasonic sensors, one or more environmental sensors, a GPS sensor, a lidar sensor, an infrared camera sensor, and a thermal camera sensor, and the UAV is further integrated with one or more microphone arrays.

[0024] In one embodiment, the lidar sensor captures accurate distance measurements, an infrared camera sensor detects heat signatures, an ultrasonic sensor monitors proximity, one or more microphone arrays record ambient noise, a thermal camera sensor detects temperature fluctuations, environmental sensors can measure parameters such as air quality, temperature, and humidity, and a GPS sensor ensures correct localization of the UAV.

[0025] In one embodiment, the data collected by the sensors (102a) is fed to a real-time data analysis module (104) comprising a machine learning-based process (104a) for detecting anomalies such as crowd panics, large partying crowds, fire outbreaks, and many forms of vandalism based on the collected data, wherein the machine learning-based processor (104a) of the real-time analysis module (104) performs anomaly detection by analyzing the patterns and deviations from normal crowd behavior derived from the collected sensor data in order to warn of a potential threat before it occurs.

[0026] In one embodiment, the machine learning-based processor (104a) of the real-time analytics module (104) is configured to use computer vision techniques to estimate crowd density and flow, with representation of this data in a predictive model enabling the system to forecast crowd movements and likely risks.

[0027] In one embodiment, the autonomous decision module (106) connected to the integrated security system (112) is further configured to independently select the best possible emergency response, including activating predefined security protocols depending on the severity of a detected threat, and wherein the microcontroller (108) activates the display unit (108a) and the audio unit (108b) based on the behavior of the crowd and the potential danger to maintain safety and security, instruct the crowd for dynamic path planning, and trigger emergency alarms.

[0028] In one embodiment, the autonomous decision module (106) is further configured to determine the most appropriate response in an emergency based on the intensity of the threat, wherein the decision module (106) ensures a system response that enables the system to reduce the risk more quickly without human intervention.

[0029] In one embodiment, the system (100) further comprises a redundant security protocol (114) to ensure that the UAVs conduct surveillance within the framework of legality and ethics and to ensure compliance with the data protection standard associated with aerial surveillance in public areas.

[0030] The present invention relates to a UAV-based surveillance system for crowd monitoring and security management. The system comprises a UAV with multiple sensors, a real-time analysis module, a decision-making module, a communication module, a microcontroller, and an integrated security system. Furthermore, the system includes a redundant security module. The UAVs monitor large crowds by collecting and processing aerial data in real time to detect potential security threats or emergencies. The integrated sensors collect detailed data on crowd density, movement patterns, and environmental conditions. This system ensures a rapid and effective response to incidents, thus improving overall security at events or public places.The UAV is designed to detect anomalies such as stampedes / crowds, large crowds, vandalism, terrorism, violent outbursts, and fires. The UAV is designed to detect these anomalies and take appropriate emergency measures to minimize damage and rescue lives. The system is capable of detecting vandalism to guide the crowd away from the vandalism. It can suggest an alternative travel route to the crowd by detecting marked events near the crowd and communicate with other monitoring systems to monitor crowd movement based on real-time data.

[0031] Fig. 2 shows a diagram of the unmanned aerial vehicle of the proposed system according to an embodiment of the present disclosure.

[0032] Fig.Figure 2 shows a fully equipped UAV with multiple sensors. The sensors include lidar (7), infrared cameras (8), ultrasonic sensors (1), microphone arrays (4, 5), thermal imaging cameras (9), environmental sensors (2), and GPS (3), which collect real-time aerial data. The UAV also has an antenna (6) for communication purposes. The sensors collect real-time aerial data and transmit precise information about the surroundings and crowd behavior. The lidar sensor captures precise distance measurements, the infrared cameras detect heat signatures, and the ultrasonic sensors monitor proximity. The microphone array records ambient noise, while the thermal imaging cameras detect temperature fluctuations. The environmental sensors measure parameters such as air quality, temperature, and humidity, which are particularly useful in assessing conditions that could compromise crowd safety. The GPS module ensures the UAV's correct positioning.The environmental sensors detect parameters that affect crowd safety, including air quality, temperature, and humidity. For example, poor air quality or extreme temperatures increase stress levels in a crowd and can lead to dangerous situations. By continuously monitoring environmental factors, the system is able to anticipate risks and issue warnings if conditions deteriorate.

[0033] The data from these numerous sensors is processed by a real-time data analytics module within the system, implemented with advanced machine learning protocols. This real-time data analytics module is specifically designed to detect anomalies such as stampedes, large crowds, fires, and many forms of vandalism. All of this is controlled by analyzing the patterns derived from this sensor data, and deviations from the normal crowd behavior are detected to warn of a potential threat before it occurs. The protocols in the machine learning process learn from history and inputs, ensuring the system is in a continuous improvement process.

[0034] The system also includes an autonomous decision-making protocol that can capture crowd behavior and analyze potential hazards. The autonomous decision-making protocol determines the optimal course of action based on detected anomalies and crowd dynamics. In addition to the sensors, a UAV, and the real-time data analysis module, the system also features a controller. This controller operates a display and audio unit to dynamically guide the crowd and generate emergency alerts. For example, if the system detects an impending stampede, it immediately broadcasts an evacuation plan via display and audio units, guiding the crowd to safety. The communication module ensures seamless interaction between the UAV and ground control stations, other UAVs, and even directly with the crowd.The communication module supports real-time communication, allowing the UAV to act as a relay for important messages or updates. The display and audio unit serve as the main interface for transmitting messages via sound or light to guide the crowd. The communication module is also capable of transmitting such notifications to private devices such as smartphones within the crowd, improving public safety by providing real-time updates of instructions.

[0035] The system also includes an integrated security system connected to the surveillance system, which coordinates response measures when threats are detected. This integrated security system acts as a subsystem that ensures all necessary measures to mitigate risks and improve public safety. If the real-time data analysis module detects a threat, the integrated security system activates predefined security protocols. These range from simply notifying local authorities to requesting additional drones to support the situation, thus ensuring a rapid and appropriate response to any type of threat.

[0036] In one embodiment, the real-time data analytics module leverages machine learning protocols within the system that utilize computer vision techniques to estimate crowd density and flow. By mapping this data into a predictive model, the system can forecast crowd movements and potential risks. For example, if the system detects an unusually high crowd density in a particular area, it predicts the possibility of congestion or panic and takes appropriate action to defuse the situation.

[0037] In one embodiment, the decision protocol autonomously determines the most appropriate response in an emergency, based on the intensity of the threat. This decision protocol ensures that the system responds quickly and without human intervention to the hazards and minimizes the risks. Alarms can be triggered, evacuation orders issued, or other safety protocols initiated to protect the crowd. The UAV surveillance system can be deployed over any public space or event of varying sizes.

[0038] In one embodiment, the UAV surveillance system modulates its surveillance activities according to the requirements of an event and the response strategies to ensure optimal coverage and effectiveness. The system features redundant security protocols to ensure that the UAVs always conduct surveillance within the framework of legality and ethics. The implemented protocols ensure compliance with data protection standards associated with aerial surveillance in public spaces. It is designed to respect the privacy of each individual while providing the greatest possible security, thus representing a reliable tool for public safety.

[0039] The present invention relates to a crowd surveillance and security system that utilizes an unmanned aerial vehicle (UAV) as a central data collection platform. The UAV is equipped with a comprehensive array of sensors, including lidar, infrared cameras, ultrasonic sensors, a microphone array, thermal imaging cameras, environmental sensors, and GPS. These sensors work together to collect real-time aerial data and capture various aspects of the crowd's environment and behavior.

[0040] The data collected by the drone's sensors is then analyzed by a real-time data analysis module. This module uses complex machine learning protocols to process the incoming information. The main function of this module is to identify anomalies in the data that could indicate potential threats. These anomalies include events such as mass panics, unusually large gatherings of people in one place, fires, and various forms of vandalism.

[0041] An autonomous decision-making protocol is integrated into the system. This protocol analyzes the processed sensor data, particularly crowd behavior, to predict potential hazards before they escalate. Based on this analysis, a controller connected to the module, the sensors, and the UAV itself activates a display unit and an audio unit. These units serve to instruct the crowd, enable dynamic path planning to avoid hazards, and trigger emergency calls if necessary.

[0042] The system also features a communications module. This module, connected to the controller, allows the UAV to communicate with ground control stations, other UAVs participating in surveillance, and even directly with the crowd. Communication with the crowd is facilitated by the display and audio units, which allow for the transmission of messages or the use of sound and light signals to convey information or instructions.

[0043] The system also includes an integrated security system connected to the surveillance system. This component ensures coordinated response measures when threats are detected. By enabling a synchronized response, the system is intended to increase public safety in congested areas.

[0044] Further system enhancements include the ability of environmental sensors to capture factors such as air quality, temperature, and humidity, providing additional context for crowd safety assessments. Machine learning protocols utilize computer vision techniques to estimate crowd density and flow and generate predictive models that anticipate movements and potential hazards. The decision protocol is designed to autonomously select the most appropriate emergency response and activate predefined safety protocols based on the severity of the detected threat. The communications module can also send notifications to smartphones or other personal devices in the crowd, providing real-time updates and guidance to further improve public safety.The UAV itself is designed to be scalable, allowing the system to be deployed in various public spaces and at events of varying sizes by adapting its surveillance activities and response strategies. Furthermore, the UAV is equipped with redundant security protocols to ensure compliance with legal and ethical standards regarding data protection in public aerial surveillance.

[0045] The drawings and the foregoing description illustrate examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the order shown; nor do all actions need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0046] Advantages, further benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, advantages, solutions to problems, and any components that may result in or enhance an advantage, advantage, or solution are not to be construed as critical, required, or essential features or components of any or all of the claims. REFERENCES 100 A Surveillance System for Crowd Control and Security 102 Unmanned Aerial Vehicle (UAV) 102a Variety of sensors 104 Real-time data analysis module 104a Processor Based on Machine Learning 106 Module on Autonomous Decision-Making 108 microcontrollers 108a Display unit 108b One audio unit 110 Communication module 112 Integrated Security System 114 Redundant security protocol

Claims

[1] A crowd control and security surveillance system comprising: an unmanned aerial vehicle (UAV) equipped with a variety of sensors and configured to collect real-time aerial data when deployed in an environment; a real-time data analysis module in conjunction with the UAV, comprising a machine learning-based processor for processing the data collected by the integrated sensor to detect anomalies, wherein the real-time data analysis module uses machine learning protocols to detect anomalies; an autonomous decision-making module in communication with the real-time data analysis module, configured to determine crowd behavior and analyze potential hazards, the autonomous decision-making module comprising a processor that determines the best course of action with respect to detected anomalies and crowd behavior based on commands received from the real-time data analysis module; a microcontroller operatively coupled to the UAV and its sensors, a real-time data analysis module, and an autonomous decision-making module, the microcontroller configured to operate a display unit and an audio unit configured to inform the crowd about the dynamic path planning and generate emergency alerts; a communication module connected to the microcontroller and configured to enable the UAV to communicate with ground control stations, other UAVs, and directly with the crowd via the display unit and the audio unit to send messages or use sound or light signals; and an integrated security system operatively connected to the autonomous decision module, the microcontroller, and the communications module and configured to coordinate response actions upon detection of a threat, wherein predefined security protocols stored in a memory are activated by the integrated security system when the autonomous decision module detects a threat. [2] The system of claim 1, wherein the plurality of sensors of the UAV comprises sensors such as ultrasonic sensors, one or more environmental sensors, a GPS sensor, a lidar sensor, an infrared camera sensor, and a thermal camera sensor, and wherein the UAV is further integrated with one or more microphone arrays. [3] The system of claim 2, wherein the lidar sensor captures accurate distance measurements, an infrared camera sensor detects heat signatures, an ultrasonic sensor monitors proximity, one or more microphone arrays record ambient noise, a thermal camera sensor detects temperature fluctuations, environmental sensors can measure parameters such as air quality, temperature and humidity, and a GPS sensor ensures correct localization of the UAV. [4] The system of claim 1 and 3, wherein the data collected by the sensors is fed to a real-time data analysis module comprising a machine learning-based process for detecting anomalies such as crowd panics, large partying crowds, fire outbreaks and many forms of vandalism based on the collected data, wherein the machine learning-based processor of the real-time analysis module performs the anomaly detection by analyzing the patterns and deviations from normal crowd behavior derived from the collected sensor data in order to warn of a potential threat before it occurs. [5] The system of claim 4, wherein the machine learning-based processor of the real-time analytics module is configured to use computer vision techniques to estimate crowd density and flow, wherein the representation of this data in a predictive model enables the system to forecast crowd movements and likely risks. [6] The system of claim 1, wherein the autonomous decision module connected to the integrated security system is further configured to independently select the best possible emergency response, including activating predefined security protocols depending on the severity of a detected threat, and wherein the microcontroller, based on the behavior of the crowd and the potential danger, activates the display unit and the audio unit to maintain safety and security, instruct the crowd for dynamic path planning, and trigger emergency alarms. [7] The system of claim 6, wherein the autonomous decision module is further configured to determine the most appropriate response in an emergency based on the intensity of the threat, the decision module ensuring a system response that enables the system to reduce the risk more quickly without human intervention. [8] The system according to claim 1 further comprises a redundant security protocol to ensure that the UAVs conduct surveillance within the framework of legality and ethics and to ensure compliance with the data protection standard associated with aerial surveillance in public areas.

Images