TY - GEN
T1 - Efficient Vehicle Counting by Eliminating Identical Vehicles in UAV aerial videos
AU - Ashutosh Holla, B.
AU - Manohara Pai, M. M.
AU - Verma, Ujjwal
AU - Pai, Radhika M.
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10/30
Y1 - 2020/10/30
N2 - Traffic surveillance using Unmanned Aerial Vehicles (UAV's) has gained a lot of attraction in civilian applications and remote sensing tasks. Thanks to its high mobility and large field of view and ability to cover regions at different altitudes UAVs have made a mark in recent years for surveillance. The primary purpose of UAV in traffic surveillance is to monitor the daily activities in the busy traffic areas and report the abnormal activities which may take place. In recent years, many gated campuses such as educational institutions, organizations, shopping malls, etc. have taken steps to keep a track of vehicles trespassing within its vicinity. Vehicle counting is one of the monitoring tasks performed in surveillance to estimate the density of vehicles in an event or areas where traffic congestion is common. In this paper, a vehicle counting framework is proposed to eliminate the problem of redundant vehicle information count when a vehicle has appeared in successive frames of UAV videos. This work demonstrates that the comparison of concatenated three features vectors (Histogram of Oriented Gradients, Local Binary Pattern, and mean RGB value) can be used to recognize identical vehicles in UAV aerial videos.
AB - Traffic surveillance using Unmanned Aerial Vehicles (UAV's) has gained a lot of attraction in civilian applications and remote sensing tasks. Thanks to its high mobility and large field of view and ability to cover regions at different altitudes UAVs have made a mark in recent years for surveillance. The primary purpose of UAV in traffic surveillance is to monitor the daily activities in the busy traffic areas and report the abnormal activities which may take place. In recent years, many gated campuses such as educational institutions, organizations, shopping malls, etc. have taken steps to keep a track of vehicles trespassing within its vicinity. Vehicle counting is one of the monitoring tasks performed in surveillance to estimate the density of vehicles in an event or areas where traffic congestion is common. In this paper, a vehicle counting framework is proposed to eliminate the problem of redundant vehicle information count when a vehicle has appeared in successive frames of UAV videos. This work demonstrates that the comparison of concatenated three features vectors (Histogram of Oriented Gradients, Local Binary Pattern, and mean RGB value) can be used to recognize identical vehicles in UAV aerial videos.
UR - http://www.scopus.com/inward/record.url?scp=85099718201&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099718201&partnerID=8YFLogxK
U2 - 10.1109/DISCOVER50404.2020.9278095
DO - 10.1109/DISCOVER50404.2020.9278095
M3 - Conference contribution
AN - SCOPUS:85099718201
T3 - 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings
SP - 246
EP - 251
BT - 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020
Y2 - 30 October 2020 through 31 October 2020
ER -
