This paper is published in Volume-5, Issue-3, 2019
Area
Computer Engineering
Author
Amritha Sharma, Milind Pathak, Avaneesh Tripathi, Parth Vijay, Shitalkumar A Jain
Org/Univ
MIT Academy of Engineering, Pune, Maharashtra, India
Keywords
YOLO, Haversine, Machine learning, Border surveillance, Neural Networks
Citations
IEEE
Amritha Sharma, Milind Pathak, Avaneesh Tripathi, Parth Vijay, Shitalkumar A Jain. Automated human detection and tracking for surveillance applications, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Amritha Sharma, Milind Pathak, Avaneesh Tripathi, Parth Vijay, Shitalkumar A Jain (2019). Automated human detection and tracking for surveillance applications. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.
MLA
Amritha Sharma, Milind Pathak, Avaneesh Tripathi, Parth Vijay, Shitalkumar A Jain. "Automated human detection and tracking for surveillance applications." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.
Amritha Sharma, Milind Pathak, Avaneesh Tripathi, Parth Vijay, Shitalkumar A Jain. Automated human detection and tracking for surveillance applications, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Amritha Sharma, Milind Pathak, Avaneesh Tripathi, Parth Vijay, Shitalkumar A Jain (2019). Automated human detection and tracking for surveillance applications. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.
MLA
Amritha Sharma, Milind Pathak, Avaneesh Tripathi, Parth Vijay, Shitalkumar A Jain. "Automated human detection and tracking for surveillance applications." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.
Abstract
Automated surveillance systems are gaining importance because of their vast applications at the border while security is concerned. Various algorithms are developed and technologies are used to improve the efficiency of these surveillance systems. Efforts are being made to reduce the number of false alarms and detect any kind of suspicious activity happening in the region of suspicion within no seconds. These suspicious activities include drug smuggling, illegal immigrants crossing the borders and last but not the least, terrorist intrusion. These activities need to be detected and analyzed in order to conclude if the activity is suspicious enough to be classified as a threat. The existing systems deployed at the border are not efficient enough to detect threats and hence this paper is designed with an objective of presenting a better algorithm to make a better automated surveillance system. The sole purpose of this algorithm is to increase security at the border because safeguarding the border till date continues to remain a challenge to our country.