Armed and Partially Covered Face Related Robberies Alerting System Using Computer Vision
Robbery is the completed or attempted theft, directly from a person, of property or cash by force or threat of force, with or without a weapon, and with or without injury. Armed robbery is a serious crime that can traumatically that emotionally and mentally profoundly traumatize its victims. Armed robbery is usually motivated by a desire to acquire money, which is then commonly used to buy drugs.  However, some armed robbers are associated with the crime.  In the current decade, armed robbery is one of the major issues in society. According to Statista research department, there were more than 0.25 million armed robberies happened in the USA in 2018.  Moreover, these robberies accounted for an estimated $438 million in losses.  . Moreover is the USA there were more than 10000 murders, victims, by weapons in 2018. When analyzing most of these armed robberies have happened locations are Banks, Gas or Service stations, and commercial houses in the USA. To monitor and act accordingly to the issue still, there is no proper method. Present in most places there are several security officers to monitor these robberies within 24H using CCTV cameras.
In this research, the authors propose a novel approach to prevent this issue using a computer vision-based armed robberies alerting system. Here this system is able to detect the weapons of the robbers. Since most of the time robbers come with partially covered faces. In this research, the proposed system is able to detect partially covered faces also. When the system identified weapons or a partially covered face in a bank or a commercial house, it will send an alert by notifying the risk of the robbery to the in house security officers and relevant authorities. The proposed solution used an object detection model and a facial landmark identification based approach to detect robbers. Because of this system, no longer the security officers need to monitor CCTV cameras by themselves
 C. J. I. S. Division, "Crime in the U.S. 2017 • Topic Pages • Robbery," 2017. [Online]. Available: https://ucr.fbi.gov/crime-in-the-u.s/2017/crime-in-the-u.s.-2017/topic-pages/robbery. [Accessed 3 9 2020].
 S. R. Department, "Number of robberies in the U.S. by weapon 2018," 11 10 2019. [Online]. Available: https://www.statista.com/statistics/251914/number-of-robberies-in-the-us-by-weapon/. [Accessed 3 9 2020].
 RCraig09, "Handguns are involved in most U.S. gun homicides," 30 1 2020. [Online]. Available: https://en.wikipedia.org/wiki/Gun_violence_in_the_United_States#/media/File:2012-_U.S._gun_murder_victims_by_weapon_(FBI_UCR).png. [Accessed 4 9 2020].
 W. E. Thornton, "Armed robbery," 10 9 2014. [Online]. Available: https://www.britannica.com/topic/armed-robbery. [Accessed 4 9 2020].
 F. team, "Robbery Overview," 20 3 2019. [Online]. Available: https://criminal.findlaw.com/criminal-charges/robbery-overview.html. [Accessed 4 9 2020].
 G. o. Netherlands, "Reducing the number of robberies," [Online]. Available: https://www.government.nl/topics/crime-and-crime-prevention/crime-prevention/reducing-the-number-of-robberies. [Accessed 04 09 2020].
 UNOWAS, "DRUG TRAFFICKING AND ORGANISED CRIME," UNOWAS, 2018. [Online]. Available: https://unowas.unmissions.org/drug-trafficking-and-organised-crime. [Accessed 9 9 2002].
 I. F. S. R. Giorgio Morales, "detecting Violent Robberies in CCTV Videos Using Deep Learning," Artificial Intelligence Applications and Innovations, pp. 282-291, 2019.
 O. D. M. M. Milagro Fernandez-Carrobles, "Gun and Knife Detection Based on Faster R-CNN for Video Surveillance," Iberian Conference on Pattern Recognition and Image Analysis, pp. 441-452, 22 9 2019.
 A. M. G. a. L. Michał Grega *, "Automated Detection of Firearms and Knives in a CCTV Image," in Sensors, 2016.
 N. ,. F. S. ,. M. M. Sulthana Abdussamed kormath, "Criminal Assault Analysis and Security Using Image Processing and Machine Learning," International Journal of Information Systems and Computer Sciences, vol. 8, pp. 35-39, 2019.
 N. d. V. L. a. M. S. Jaime Dever, "Automatic Visual Recognition of Armed Robbery," [Online]. Available: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.424.7992&rep=rep1&type=pdf. [Accessed 7 9 2020].
 B. M. a. M. T. M. H. Qezavati, "Partially Covered Face Detection in Presence of Headscarf for Surveillance Applications," in 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA) - IEEE, ehran, Iran, 2019.
 K. H. G. M. R. Tasriva Sikandar, "ATM crime detection using image processing integrated video surveillance: a systematic review," in Multimedia Systems, 2018.
 M. A. M. N. H. a. M. Y. A. Warsi, "Automatic Handgun and Knife Detection Algorithms: A Review,," in 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), Taichung, Taiwan, 2020.
 A. F. Joseph Redmon, "YOLOv3: An Incremental Improvement," University of Washington, Washington, 2018.
 C. Sagonas, "Facial point annotations," [Online]. Available: https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/. [Accessed 12 9 2020].
 H. R. Seyed Reza Salari, "Pgu-Face:A Dataset of Partially covered facial images," Data in Brief, pp. 288-291, 2016.
 Caught on Camera , "CCTV Legal Requirements: CCTV Laws Explained," Caught on Camera , 2020. [Online]
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
To ensure uniformity of treatment among all contributors, other forms may not be substituted for this form, nor may any wording of the form be changed. This form is intended for original material submitted to AJCT and must accompany any such material in order to be published by AJCT. Please read the form carefully.
The undersigned hereby assigns to the Asian Journal of Convergence in Technology Issues ("AJCT") all rights under copyright that may exist in and to the above Work, any revised or expanded derivative works submitted to AJCT by the undersigned based on the Work, and any associated written, audio and/or visual presentations or other enhancements accompanying the Work. The undersigned hereby warrants that the Work is original and that he/she is the author of the Work; to the extent the Work incorporates text passages, figures, data or other material from the works of others, the undersigned has obtained any necessary permission. See Retained Rights, below.
AJCT distributes its technical publications throughout the world and wants to ensure that the material submitted to its publications is properly available to the readership of those publications. Authors must ensure that The Work is their own and is original. It is the responsibility of the authors, not AJCT, to determine whether disclosure of their material requires the prior consent of other parties and, if so, to obtain it.
RETAINED RIGHTS/TERMS AND CONDITIONS
1. Authors/employers retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors/employers may reproduce or authorize others to reproduce The Work and for the author's personal use or for company or organizational use, provided that the source and any AJCT copyright notice are indicated, the copies are not used in any way that implies AJCT endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Authors/employers may make limited distribution of all or portions of the Work prior to publication if they inform AJCT in advance of the nature and extent of such limited distribution.
4. For all uses not covered by items 2 and 3, authors/employers must request permission from AJCT.
5. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
INFORMATION FOR AUTHORS
AJCT Copyright Ownership
It is the formal policy of AJCT to own the copyrights to all copyrightable material in its technical publications and to the individual contributions contained therein, in order to protect the interests of AJCT, its authors and their employers, and, at the same time, to facilitate the appropriate re-use of this material by others.
If you are employed and prepared the Work on a subject within the scope of your employment, the copyright in the Work belongs to your employer as a work-for-hire. In that case, AJCT assumes that when you sign this Form, you are authorized to do so by your employer and that your employer has consented to the transfer of copyright, to the representation and warranty of publication rights, and to all other terms and conditions of this Form. If such authorization and consent has not been given to you, an authorized representative of your employer should sign this Form as the Author.
AJCT requires that the consent of the first-named author and employer be sought as a condition to granting reprint or republication rights to others or for permitting use of a Work for promotion or marketing purposes.
1. The undersigned represents that he/she has the power and authority to make and execute this assignment.
2. The undersigned agrees to indemnify and hold harmless AJCT from any damage or expense that may arise in the event of a breach of any of the warranties set forth above.
3. In the event the above work is accepted and published by AJCT and consequently withdrawn by the author(s), the foregoing copyright transfer shall become null and void and all materials embodying the Work submitted to AJCT will be destroyed.
4. For jointly authored Works, all joint authors should sign, or one of the authors should sign as authorized agent
for the others.
Licenced by :