The use of surveillance cameras has proliferated in both public and private sectors, aiming to enhance security in various environments. However, relying solely on human monitors to oversee these video feeds introduces the risk of human error, where critical events might be overlooked. This challenge has spurred the development of automated systems for crime detection using neural networks, significantly improving the ability to identify and respond to criminal activities swiftly and accurately .
Objective and Approach
The primary goal of the research is to develop an intelligent crime-detection system that integrates with smart city infrastructures. The system focuses on detecting specific types of crimes such as arson, burglary, and vandalism through the analysis of surveillance video feeds. By employing multiple deep learning models, the research aims to provide an efficient and adaptable crime-detection mechanism that reduces the dependency on human surveillance and enhances the responsiveness of law enforcement agencies .
Methodology
The methodology involves training several deep learning models to recognize and classify criminal activities from video inputs. The crimes of interest in this study include arson, burglary, and vandalism. Each crime type was addressed using different models trained on specific datasets:
- Arson Detection: Videos were processed using YOLOv5.
- Burglary Detection: YOLOv7 was employed for training.
- Vandalism Detection: YOLOv6 was used for this category.
These models were chosen based on their ability to handle specific crime-related objects and actions effectively. The performance of these models was measured using metrics such as mean Average Precision (mAP), with YOLOv7 achieving the highest mAP of 87 for burglary detection .
System Design
The system was designed to automatically identify crime types based on surveillance video inputs and notify registered users about detected crimes via SMS alerts. This involved several steps:
- Data Collection and Processing: Videos were annotated to label and classify objects and actions relevant to the crimes of interest. The data was then divided into training, validation, and test sets.
- Model Training and Evaluation: Various models were trained and their performance evaluated using metrics like mAP, precision, and recall. The models were fine-tuned to improve their accuracy and robustness.
- Integration and Deployment: The trained models were integrated into a web-based interface using Gradio, enabling users to upload videos for analysis. The system was connected to the Twilio communication tool to send alerts to users when suspicious activities were detected.
Results and Performance
The research highlighted the effectiveness of using advanced machine learning models for crime detection. The system was able to classify crimes accurately and send timely alerts to users, thus enhancing the security infrastructure of smart cities. The models showed varying performance metrics:
- YOLOv5: Achieved 80% mAP for arson detection.
- YOLOv7: Highest performance with 87% mAP for burglary detection.
- YOLOv6: Achieved 86% mAP for vandalism detection .
These models were able to process and analyze video inputs in real-time, providing a practical solution for automated crime detection.
Implementation and User Interface
The implementation involved creating a user-friendly interface using Gradio, which allowed users to interact with the system easily. Users could upload surveillance videos and receive analysis results along with alert notifications in case of detected crimes. The system’s backend managed the video processing, model inference, and alert generation, ensuring a seamless user experience .
Challenges and Future Work
One of the main challenges faced during the development was ensuring the models’ accuracy across different environmental conditions, such as varying lighting and camera angles. Future work will focus on improving the models’ robustness and expanding the range of detectable crimes. Additionally, integrating more advanced features like real-time tracking and multi-camera coordination could further enhance the system’s capabilities .
Conclusion
The development of a camera-based crime behavior detection and classification system represents a significant advancement in the field of smart city security. By leveraging deep learning models, the system offers an automated, efficient, and accurate method for detecting and responding to criminal activities. This not only alleviates the burden on human monitors but also ensures quicker and more reliable crime detection, ultimately contributing to safer communities.
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