AI-INTEGRATED LITTER SURVEILLANCE SYSTEM FOR CONTROLLED ENVIRONMENTS

Abstract

The AI-Integrated Litter Surveillance System for Controlled Environments is a groundbreaking solution designed to tackle the persistent problem of improper waste disposal in confined spaces such as gated communities, college campuses, and similar controlled environments. This innovative system seamlessly integrates Artificial Intelligence (AI) and Closed-Circuit Television (CCTV) technology to monitor, identify, and deter individuals engaging in littering activities, particularly from vehicles. Leveraging existing or strategically positioned CCTV cameras, the system employs advanced AI algorithms for real-time object recognition, with a primary focus on the precise identification of vehicles associated with littering incidents. The technology captures vital vehicle details, including license plates, enabling comprehensive documentation of offenses. The system operates through an automated notification and penalty framework, instantly alerting relevant authorities or property management upon detection of a littering event and implementing penalties based on the recorded evidence. The user-friendly interface facilitates efficient management of recorded data, providing property owners, security personnel, and authorities with a streamlined process for reviewing incidents and taking appropriate actions. This AI-Integrated Litter Surveillance System for Controlled Environments not only revolutionizes waste management practices but also serves as a proactive tool to ensure environmental compliance and promote responsible waste disposal within confined areas. 6 claims and 2 figures

Specification

Description:AI-INTEGRATED LITTER SURVEILLANCE SYSTEM FOR CONTROLLED ENVIRONMENTS
Field of the Invention
The proposed invention falls under the field of AI (Artificial Intelligence) and specifically involves applications related to computer vision and machine learning, with a specific focus on solving a real-world problem related to waste disposal through the integration of these technologies.
Objectives of the Invention
The objective of the proposed idea is to create an innovative and automated Waste Disposal Monitoring System using Artificial Intelligence (AI) and Closed-Circuit Television (CCTV) integration. The primary goal is to address the pervasive issue of improper waste disposal, especially in public areas like gated communities, college campuses, or other confined spaces where littering is a concern. The system aims to achieve the following objectives: Preventing improper waste disposal, Utilizing CCTV cameras for surveillance, and Vehicle identification and documentation.

Background of the Invention
The persistent and widespread issue of littering in public spaces has spurred the development of the AI-Integrated Litter Surveillance System for Controlled Environments. Traditional methods of addressing this problem have often proven inadequate, leading to environmental degradation, health hazards, and compromised aesthetics. Recognizing the need for a more sophisticated and proactive approach, the invention focuses on controlled environments, such as gated communities and educational institutions, where targeted surveillance and enforcement measures can be more effectively implemented. By addressing littering in these specific settings, the system aims to create cleaner and more aesthetically pleasing spaces, contributing to the overall well-being and satisfaction of residents or occupants. The prevalence of littering underscores the urgency for innovative solutions, and the proposed system seeks to fill this gap by integrating cutting-edge technologies like Artificial Intelligence and Machine Learning with robust surveillance infrastructure to tackle litter at its source.
For instance, CN110723433A discloses the present disclosure discloses a garbage classification and recovery method and system. The method comprises the following steps: acquiring identity information of a person throwing garbage to be classified; detecting the garbage to be classified to obtain detection data of the garbage to be classified analysing the detection data to obtain a spam inspection conclusion: namely determining that the garbage to be classified belongs to one of mixed garbage or non-mixed garbage; processing the garbage to be classified according to a garbage checking conclusion; and binding the identity information, the detection data and the spam verification conclusion. According to the technical scheme, the throwing persons can be traced from the starting of the garbage, particularly, the throwing persons can be traced under the condition that the inspection result is the mixed garbage, and the garbage classification measures are favourably implemented.
Similarly, the US20200082167A1 system and process for trash-can management. The process uses digital images to extract trash cans from the images and a classifier to determine the trash can's state. The process can include responses to trash cans that need servicing. A neural network machine learning algorithm is used to identify trash cans in the image. Neural network classifiers are used to classify the state of the identified cans. The neural networks are trained with images containing trash cans and the surrounding area that has trash and does not have trash to determine a binary state. Trash cans identified with a low confidence level can be used to retrain the neural networks. The process can include the management of the trash can by generating reports, maps, notifications, collection routes, or assigning workers.
CN109389161B is also related to litter detection, The invention discloses a garbage identification evolutionary learning method, a device, a system and a medium based on deep learning, wherein the method comprises the following steps: acquiring image data of a garbage sample; preprocessing image data of the garbage sample; taking the pre-processed image data of the garbage sample as an input parameter of a neural network, comparing the input parameter with a trained garbage recognition model, and judging whether recognition is successful or not according to a comparison result; feeding back corresponding information of the successfully identified garbage to the garbage classification throwing mechanism; identifying the garbage sample image data which is failed to be identified again through a ResNet algorithm, marking the garbage sample image data which is successfully identified by the ResNet algorithm, feeding corresponding garbage information back to a garbage classification throwing mechanism, and updating a garbage identification model; and transmitting the garbage sample image data successfully identified by the ResNet algorithm to a user or a maintenance person for marking, and updating the garbage identification model. The invention greatly reduces the workload of maintenance personnel and realizes the accurate classification of a large amount of garbage.
Also US10977917B2, A camera transmits a captured image of a surveillance area to a server. A microphone receives a sound from the surveillance area and transmits the sound to the server. The server detects a warning sound starting based on a harming behaviour on a vehicle in a surveillance area and arriving from the vehicle using the sound of the surveillance area, determines an occurrence position of the harming behaviour based on the detection of the warning sound, acquires a captured image of the occurrence position, determines the vehicle on which the harming behaviour is executed using the captured image of the occurrence position, acquires information regarding the vehicle, and records the information regarding the vehicle and the captured image of the occurrence position in association with each other in an accumulator.
The proposed AI-Integrated Litter Surveillance System for Controlled Environments introduces a novel and comprehensive approach to combat littering, differentiating itself from existing patents. While focusing on garbage classification and recovery by acquiring identity information and analysing detection data, our system goes beyond by integrating AIML algorithms with CCTV cameras to precisely detect and attribute littering instances to specific vehicles in controlled environments. Similarly, the employs digital images and classifiers for trash-can management, yet our invention enhances the surveillance scope by incorporating real-time notifications, enforcement mechanisms, and a comprehensive data analytics and reporting tool. The patent revolves around garbage identification using deep learning; our system extends this concept by incorporating live video feeds, ensuring a proactive response to littering incidents with traceable vehicle identification. Furthermore, the involves surveillance using cameras and microphones to detect harmful behaviours on vehicles. In contrast, our invention specifically targets littering instances, providing a sophisticated and tailored solution for maintaining cleanliness in designated areas.
Summary of the Invention
The proposed invention is an automated AI-Integrated Litter Surveillance System for Controlled Environments to effectively combat improper waste disposal in specific locations like gated communities or college campuses. Utilizing existing or strategically placed CCTV cameras, the system employs advanced AI and machine learning algorithms for real-time object recognition, specifically trained to identify instances of waste disposal and distinguish normal activities. The focus is on capturing vehicle information, including license plates, to document and penalize offenders automatically. With an intuitive user interface, the system facilitates easy management of recorded data, generating notifications for relevant authorities and implementing a penalty system. The invention represents a significant advancement in waste management, addressing environmental concerns and promoting compliance with waste disposal regulations through innovative AI and CCTV integration.
Brief Description of Drawings
The invention will be described in detail with reference to the exemplary embodiments shown in the figures wherein:
Figure-1: Architecture and brief working of the proposed system
Figure-2: Diagrammatic representation of AI-Integrated Litter Surveillance System.
Detailed Description of the Invention
The AI-Integrated Litter Surveillance System for Controlled Environments comprises a comprehensive set of software and hardware components, each playing a crucial role in the effective functioning of the system. This integrated solution leverages advanced technologies to seamlessly detect, monitor, and prevent littering within designated areas.
Hardware Components: CCTV Cameras, High-resolution cameras are strategically placed to cover the entire controlled environment. Equipped with low-light and infrared capabilities for 24/7 surveillance. Pan-tilt-zoom (PTZ) features for dynamic monitoring. A powerful processing unit responsible for real-time video data analysis. Utilizes advanced graphics processing units (GPUs) for accelerated machine learning computations. Sufficient memory and storage capacity to handle large volumes of video data. High-capacity storage devices for the secure and organized storage of video footage, timestamps, and identified vehicle information. Integration with cloud storage for scalability and redundancy. Robust networking infrastructure to ensure seamless communication between CCTV cameras, processing units, and the central server. High-speed internet connectivity for real-time data transmission.
AIML Algorithms and machine learning algorithms are designed for image recognition and classification. Trained models to distinguish between normal activities and instances of littering. Constantly evolving algorithms for continuous improvement based on real-world data.Specialized software for vehicle identification using computer vision techniques. Integration with image processing libraries for accurate and rapid analysis. Algorithms to track and trace vehicles in real-time.Customized software module for detecting and flagging littering events. Threshold settings to minimize false positives and negatives. Timestamp synchronization for precise event logging. Analytical tools for extracting meaningful insights from collected data. Reporting features to generate comprehensive reports on littering trends, peak times, and identified vehicles. User-friendly dashboard for easy interpretation of analytics. Automated notification system to alert relevant authorities upon the detection of littering incidents. Customizable notification preferences and escalation procedures.
Software module allowing for the implementation of enforcement measures. Integration with databases to manage and track penalties, fines, or warnings. Historical data analysis for assessing the effectiveness of enforcement actions. Robust security measures, including encryption protocols, to protect sensitive data. Access control mechanisms to restrict system access to authorized personnel only.The "AI-Integrated Litter Surveillance System for Controlled Environments" is a revolutionary invention designed to combat the pervasive issue of littering in public spaces, focusing specifically on controlled settings such as gated communities and educational institutions. Traditional methods of curbing littering have proven inadequate, necessitating a more sophisticated approach that leverages cutting-edge technology. This system proposes the integration of Artificial Intelligence and Machine Learning (AIML) with Closed-Circuit Television (CCTV) cameras strategically placed within the monitored environment.At its core, the system employs state-of-the-art AIML algorithms to analyze real-time video data captured by CCTV cameras. The primary objective is to identify instances of littering and attribute them to specific vehicles within the controlled environment. By harnessing computer vision techniques, the system ensures accurate identification of vehicles, allowing for precise monitoring and enforcement. This integration significantly enhances the ability to distinguish between normal activities and intentional littering, thereby reducing false positives and optimizing the efficiency of the surveillance system.In practical terms, the system operates seamlessly, continuously scanning the monitored area for any signs of littering. When a littering event is detected, the system captures essential information, including the vehicle's identification, timestamp, and video footage of the incident. This data is then securely stored for further analysis and potential enforcement actions. By centralizing this information, authorities can respond promptly and effectively, taking appropriate measures to address the littering incident. The system's ability to provide accurate and timely data ensures that environmental cleanliness is maintained within the controlled environment.
A critical aspect of the proposed invention lies in its notification and enforcement capabilities. Upon the detection of littering, the system can automatically trigger notifications to relevant authorities, alerting them to the incident. Additionally, the system offers the option to implement enforcement measures, such as issuing fines or warnings. This dual functionality ensures a holistic approach to litter prevention, combining both reactive and proactive measures to discourage littering behaviour effectively.
To ensure the success and scalability of the AI-Integrated Litter Surveillance System, the invention incorporates lessons from advancements in computer vision for surveillance systems and machine learning algorithms for video analysis in environmental monitoring. These references inform the design and implementation of the system, ensuring that it aligns with industry best practices and remains at the forefront of technological innovation in litter prevention.Finally, the AI-Integrated Litter Surveillance System for Controlled Environments represents a significant leap forward in the quest to mitigate the impact of littering. By combining the power of AIML with sophisticated CCTV technology, this invention addresses the unique challenges posed by controlled environments, offering a comprehensive solution to maintain cleanliness, uphold environmental standards, and create a more sustainable and aesthetically pleasing living or educational space.
The AI-integrated litter Surveillance System for Controlled Environments boasts numerous advantages, revolutionizing litter management in various ways. Firstly, the system provides unparalleled accuracy and efficiency in litter detection. By leveraging advanced AIML algorithms and computer vision, it can swiftly identify instances of littering, distinguishing them from routine activities. This accuracy minimizes false positives, ensuring that enforcement actions are targeted and effective.The system introduces a proactive and real-time response mechanism to combat littering. The moment a littering event is detected, automated notifications are sent to relevant authorities, enabling swift intervention. This rapid response not only addresses the immediate concern but also serves as a powerful deterrent, discouraging potential litterers within the controlled environment.Another significant advantage lies in the system's ability to attribute littering incidents to specific vehicles. Through precise vehicle identification using CCTV cameras, the system ensures accountability. This feature facilitates targeted enforcement measures, such as issuing fines or warnings to the responsible parties. The traceability provided by the system enhances the overall efficacy of anti-littering efforts.
The AI-Integrated Litter Surveillance System enables data-driven decision-making. The collected data and analytics offer insights into littering patterns, peak times, and hotspots. This information empowers authorities to implement strategic interventions, allocate resources efficiently, and tailor anti-littering campaigns based on real-world data. It provides a proactive approach to environmental cleanliness management within a controlled environment.The system contributes to a safer and cleaner living or educational space. By actively discouraging littering behaviour and enforcing consequences, the invention fosters a sense of community responsibility. A cleaner environment not only enhances the quality of life for residents or students but also promotes a positive image of the controlled area. Overall, the AI-Integrated Litter Surveillance System stands as a technological advancement with the potential to significantly improve the cleanliness and aesthetics of controlled environments.
6 Claims and 2 Figures
Equivalents
The present invention,An AI-Integrated Litter Surveillance System for Controlled Environments uses real-time analytics and sophisticated picture recognition to automatically identify, assess, and control litter in specific areas, guaranteeing maximum cleanliness and economical use of resources. , Claims:The scope of the invention is defined by the following claims:

Claim
1. A Leading AI In Early Disease Detection with Surveil Health Applicants comprising:
a) A System for Litter Surveillance: This claim encompasses the comprehensive integration of Artificial Intelligence and Machine Learning (AIML) algorithms with strategically placed Closed-Circuit Television (CCTV) cameras for the explicit purpose of real-time litter detection. The system utilizes advanced image analysis and vehicle identification techniques to accurately identify instances of littering within controlled environments.
b) The present invention provides a comprehensive system for litter surveillance through the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms with strategically deployed Closed-Circuit Television (CCTV) cameras. This system is specifically designed to facilitate real-time detection of littering activities within controlled environments. Utilizing advanced image analysis techniques, the system identifies instances of litter in inappropriate locations and, through vehicle identification algorithms, associates these incidents with potential offending vehicles, where applicable.
c) The system's configuration further incorporates classification models trained on diverse litter types and employs motion detection capabilities to enhance accuracy, minimize false positives, and ensure reliable litter monitoring.
2. According to claim 1, the system further includes a notification module that triggers alerts to designated personnel upon detecting littering activity. This module enables immediate response actions by transmitting alerts via SMS, email, or through a connected mobile application, ensuring timely management and cleanup of litter in the monitored area.
3. As per claim 1, vehicle Identification Technology: In this claim, the focus is on the advanced computer vision techniques employed by the system for precise vehicle identification. The technology includes features such as Pan-Tilt-Zoom (PTZ) capabilities to dynamically monitor the environment and ensure accurate tracking of vehicles, contributing to the system's overall efficiency.

4. According to claim 1, the vehicle identification technology also leverages real-time data processing to swiftly analyze vehicle characteristics, such as license plate details and movement patterns, further ensuring precise identification and tracking within diverse environments.
5. As per claim 1, the system employs machine learning algorithms to continuously improve vehicle recognition accuracy by learning from previous detection's, adapting to various lighting and weather conditions to maintain high performance.
6. According to claim 1, the integration of this technology allows for seamless collaboration with other surveillance components, enabling comprehensive monitoring and facilitating efficient data sharing across the system for enhanced situational awareness.

Documents