CrimeSense: A High-Accuracy Video Crime Classification System

Abstract

In this paper, a deep learning based automated crime classification system called CrimeSense is proposed. CrimeSense employs a transfer learning approach utilizing a MobileNetV2 base model fine-tuned with additional convolutional layers to extract robust features from video frames. This approach facilitates the classification of various criminal activities depicted in videos. The system achieves a remarkable accuracy of 97.8% on the UCF Crime Dataset, demonstrating its potential as a useful instrument for law enforcement and other stakeholders in video-based crime analysis. In this paper, a deep learning based automated crime classification system called CrimeSense is proposed.

Introduction

The growing volume of video data from surveillance systems necessitates automated methods to identify and classify criminal activities efficiently, as manual analysis is slow and error-prone. Traditional machine learning approaches using handcrafted features have limited success due to the complexity of crime patterns. Deep learning, particularly convolutional neural networks (CNNs), has emerged as a superior solution, enabling more accurate detection.

The proposed system, CrimeSense, utilizes advanced deep learning techniques, including transfer learning with MobileNetV2 and custom convolutional layers, to achieve high accuracy in video crime classification. Training on the UCF Crime Action Dataset, CrimeSense outperformed earlier models, reaching an accuracy of 97.8%.

CrimeSense processes video by extracting frames, applying preprocessing, making frame-wise crime predictions, and aggregating these to classify the overall video. This system offers law enforcement a powerful tool for timely crime detection and prevention, improving public safety. The paper also reviews related research, noting the evolution from traditional machine learning to deep learning architectures like 3D CNNs, LSTM networks, and object detection models such as YOLOv5, highlighting their benefits and limitations.

In summary, CrimeSense represents a significant advancement in automated crime detection through video analytics, combining state-of-the-art deep learning methods to address the challenges posed by large-scale surveillance data.

Conclusion

In the era of burgeoning digital surveillance and escalating concerns over public safety, the development of advanced technologies for automated video crime classification stands as a paramount imperative. This paper presents CrimeSense, a high-accuracy video crime classification system that leverages deep learning techniques to analyse and categorize criminal activities depicted in video footage. By means of rigorous testing and iterative improvement, CrimeSense surpasses the performance limitations of the prior models and proves the usefulness of deep learning methods in real-world crime detection applications, achieving an outstanding accuracy of 97.8% on the UCF Crime Action Dataset. The journey towards the development of CrimeSense has been characterized by a systematic exploration of model architectures, training methodologies, and dataset characteristics. By integrating transfer learning techniques, custom convolutional layers, and principled training processes, CrimeSense embodies the culmination of cutting-edge research in the field of automated video crime classification. The system\'s ability to automate labour-intensive tasks associated with manual video analysis, expedite investigations, and enhance operational efficiency positions it as a valuable asset for law enforcement agencies and security personnel. While CrimeSense represents a significant advancement in video-based crime analysis, it\'s essential to acknowledge potential limitations and avenues for future research. The quality and diversity of the training data are intrinsically linked to the model\'s performance; hence efforts must be made continuously to expand the dataset with more diverse examples. Furthermore, enhancing the model\'s interpretability and generalizability to unseen crime scenarios remains an important area for future exploration. In conclusion, CrimeSense offers a compelling solution to the challenges of automated video crime classification, with far-reaching implications for public safety and security. By fostering collaboration between researchers, practitioners, and policymakers, CrimeSense has the potential to revolutionize law enforcement operations and contribute to the creation of safer, more secure communities in the digital age. As we continue to push the boundaries of AI-driven technologies, CrimeSense stands as a testament to the transformative power of innovation in addressing complex societal challenges.

References

[1] Dilip Kumar Sharma,Aaditya Yadav,DeepMask Transforming Face Mask Identification for Better Pandemic Control in the COVID 19 Era,2024. [2] LAURA MURPHY, Using Machine Learning to Optimize Near-Earth Object Sighting Data at the Golden Ears Observatory,2023 [3] Sedat Kilic, Mihran Tuceryan, Crime Detection from Pre-crime Video Analysis with Augmented Pose Information, 2023. [4] Sedat Kilic, Mihran Tuceryan, Crime Detection from Pre-crime Video Analysis with Augmented Pose Information,2023. [5] Surbhi Singla, Raman Chadha, Detecting Criminal Activities From CCTV by using Object Detection and machine Learning Algorithms, 2023. [6] Surbhi Singla, Raman Chadha, Detecting Criminal Activities From CCTV by using Object Detection and machine Learning Algorithms,2023. [7] MEHSEN, RADDAM SAMI, Deep Learning Algorithm for Detecting and Analyzing Criminal Activity, 2023. [8] Ali Mansour Al-Madani, Vivek Mahale, Ashok T. Gaikwad, Real-Time Detection of Crime and Violence in Video Surveillance using Deep Learning, 2023. [9] Vijeikis, Romas, Vidas Raudonis, and Gintaras Dervinis, Efficient violence detection in surveillance, 2022. [10] Md. Muktadir Mukto, Mahamudul Hasan, Md. Maiyaz Al Mahmud, Ikramul Haque, Md. Ahsan Ahmed, Taskeed Jabid, Md. Sawkat Ali, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam, Maheen Islam, Design of a real-time crime monitoring system using deep learning techniques, 2024. [11] Pawan Whig, Nuno Silva, Ahmed A. Elngar, Nagender Aneja, Pavika Sharma, Sustainable Development through Machine Learning, AI and IoT, 2023. [12] Kareem El-Mallahy, Crime Detection Using Deep learning, 2022 [13] Dickson Despommier,The Rise of Vertical Farms,2009 [14] Mohammed Boukabous, Mostafa Azizi, Image and video-based crime prediction using object detection and deep learning, 2023. [15] Mohammed Boukabous, Mostafa Azizi, Image and video-based crime prediction using object detection and deep learning, 2023. [16] Kishan Bhushan Sahay, Bhuvaneswari Balachander, B. Jagadeesh, G. Anand Kumar, Ravi Kumar, L. Rama Parvathy, A real time crime scene intelligent video surveillance systems in violence detection framework using deep learning techniques, 2022. [17] Shailesh Tiwari, Munesh C. Trivedi, Mohan L. Kolhe, Brajesh Kumar Singh, Advances in Data and Information Sciences, 2023. [18] Ravinarayana B, Shakhin M, Shetty Pooja, Shreya Shetty, Yashodha Ambig, Crime Activity Detection Using Machine Learning, 2022.

Copyright

Copyright © 2025 Shekapuram Lokesh Chandra, Jagarlamudi Adithya, Vecha Sai Badarinadh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.