Crime Detection and Evidence Extraction Using Machine Learning on Cloud
DOI:
https://doi.org/10.61841/wnd3zn72Keywords:
detection, evidence extraction, learningAbstract
Digital video plays a vital role in evidence identification, analysis, presentation, and report recently. With the convenience use of smartphones and the increasing popularity of surveillance camera, visual data are hugely being used in digital crime investigation. Extracting evidence becomes a time-consuming process when searched manually. To ensure reduction in possible human errors and for better accuracy, machine learning model is used which speeds up the investigation by extracting crime scene objects and masked faces from the crime scene footages. The main approach of this paper is to develop such machine learning model to assist forensic investigation for better evidence extraction with higher accuracy. In order to further reduce the processing time of the object extraction model, parallel processing is introduced which helps the model to perform faster in larger data sets. The evidence extracted from the video are stored in the cloud storage repository. Storing of these extracted evidences in the cloud helps to compare the extracted evidences with the past crimes and these extracted crime scene objects can be used to train the model further in order to improve the detection rate.
Downloads
References
1. Mohamad Firham Efendy Md Senan, Siti Norul Huda Sheikh Abdullah, Wafa Mohd Kharudin, Nur Afifah Mohd Saupi. “CCTV Quality Assessment for Forensics Facial Recognition Analysis.” In 2017 7th International Conference on Cloud Computing, Data Science & Engineering, pp.649-655. IEEE, 2017.
2. Jianyu Xiao, Shancang Li, Qingliang Xu. “Video-Based Evidence Analysis and Extraction in Digital Forensic Investigation”, Special Section on Deep Learning: Security and Forensics Research Advances and Challenges. IEEE, 2019.
3. Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi. “You Only Look Once: Unified, Real-Time Object Detection.” arXiv preprint arXiv: 1506.02640v5, 2016
4. Muhammad Usman Yaseen, Ashiq Anjum, Omer Rana, and Nikolaos Antonopoulos. “Deep Learning Hyper-Parameter Optimization for Video Analytics in Cloud” In Systems, Man, And Cybernetics: Systems, pp.1-12. IEEE, 2018.
5. Jinna Lv, Bin Wu, Chang Liu, Xiaoyan Gu. “PF-Face: A Parallel Framework for Face Classification and Search from Massive Videos Based on Spark.” In 2018 IEEE Fourth International Conference on Multimedia Big Data, IEEE, 2018.
6. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. “ImageNet Classification with Deep Convolutional Neural Networks.” papers.nips.cc. https://papers.nips.cc/paper/4824 -imagenet-classification-with-deep- convolutionalneural-networks.pdf
7. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.” Deep Residual Learning for Image Recognition” arXiv preprint arXiv: 1512.03385v1, 2015
8. Yuyong Cui, Zhiyuan Zeng, Liu Liu “An Object Detection Algorithm Based on the Cloud Model” In
9. Anjum, T. Abdullah, M. Tariq, Y. Baltaci, and N. Antonopoulos, “Video stream analysis in clouds: An object detection and classification framework for high performance video analytics.” IEEE Trans. Cloud Computing. IEEE, 2016.
10. Seoyoung Choi, Eli Salter, Xuyun Zhang, Burkhard C. Wunsche. “Bird Eyes: A CloudBased Object Detection System for Customisable Surveillance” In 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2018.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
