TY - GEN
T1 - Behavior recognition of moving objects using deep neural networks
AU - Zhu, Jiasong
AU - Lin, Weidong
AU - Sun, Ke
AU - Hou, Xianxu
AU - Liu, Bozhi
AU - Qiu, Guoping
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - With the rapid development of modern road traffic network, the demand of automatic traffic understanding has become a vital issue for building the intelligent traffic monitoring system and self-driving techniques. In this paper, we focus on behavior recognition of moving objects at busy road intersections in a modern city. To achieve this, we first capture a 4K (3840×2160) traffic video at a busy road intersection of a modern megacity by flying an UAV during the rush hours, and then manually annotate locations and types of road vehicles to form a dataset for this research. Next we propose an innovative behavior recognition framework consists of advanced deep neural network based vehicle detection and localization, type (car, bus and truck) recognition, tracking and behavior recognition over time. We will present experimental results to demonstrate the effectiveness of our solution. This paper not only demonstrates the advantages of using the latest technological advancements (4K video and UAV) but also provides an advanced deep neural network based solution for exploiting these technological advancements for urban traffic analysis.
AB - With the rapid development of modern road traffic network, the demand of automatic traffic understanding has become a vital issue for building the intelligent traffic monitoring system and self-driving techniques. In this paper, we focus on behavior recognition of moving objects at busy road intersections in a modern city. To achieve this, we first capture a 4K (3840×2160) traffic video at a busy road intersection of a modern megacity by flying an UAV during the rush hours, and then manually annotate locations and types of road vehicles to form a dataset for this research. Next we propose an innovative behavior recognition framework consists of advanced deep neural network based vehicle detection and localization, type (car, bus and truck) recognition, tracking and behavior recognition over time. We will present experimental results to demonstrate the effectiveness of our solution. This paper not only demonstrates the advantages of using the latest technological advancements (4K video and UAV) but also provides an advanced deep neural network based solution for exploiting these technological advancements for urban traffic analysis.
KW - Behavior Recognition
KW - Deep Neural Networks
KW - Long Short Term Memory
KW - Unmanned Aerial Vehicles (UAV)
KW - Vehicle Detection
KW - Vehicle Tracking
UR - http://www.scopus.com/inward/record.url?scp=85060308316&partnerID=8YFLogxK
U2 - 10.1109/SmartWorld.2018.00043
DO - 10.1109/SmartWorld.2018.00043
M3 - Conference Proceeding
AN - SCOPUS:85060308316
T3 - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
SP - 45
EP - 52
BT - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
A2 - Loulergue, Frederic
A2 - Wang, Guojun
A2 - Bhuiyan, Md Zakirul Alam
A2 - Ma, Xiaoxing
A2 - Li, Peng
A2 - Roveri, Manuel
A2 - Han, Qi
A2 - Chen, Lei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
Y2 - 7 October 2018 through 11 October 2018
ER -