Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning

Imran Ahmed; Sadia Din; Gwanggil Jeon; Francesco Piccialli; Giancarlo Fortino

doi:10.1109/JAS.2020.1003453

Volume 8 Issue 7

Jul. 2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(7): 1253-1270

I. Ahmed, S. D. D. n, G. Jeon, F. Piccialli, and G. Fortino, "Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1253-1270, Jul. 2021. doi: 10.1109/JAS.2020.1003453

Citation:

I. Ahmed, S. D. D. n, G. Jeon, F. Piccialli, and G. Fortino, "Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1253-1270, Jul. 2021. doi: 10.1109/JAS.2020.1003453

Citation:

I. Ahmed, S. D. D. n, G. Jeon, F. Piccialli, and G. Fortino, "Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1253-1270, Jul. 2021. doi: 10.1109/JAS.2020.1003453

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Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning

doi: 10.1109/JAS.2020.1003453

1.
Center of excellence in Information Technology, Institute of Management Sciences, Peshawar 25000, Pakistan
2.
Department of Information and Communication Engineering, Yeungnam University, South Korea
3.
School of Electronic Engineering, Xidian University, Xi’an 710071, China
4.
Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea
5.
Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Napoli 80138, Italy
6.
Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, CS 87036, Italy

Funds: This work was supported by the Framework of International Cooperation Program managed by the National Research Foundation of Korea (2019K1A3A1A8011295711)

More Information

Author Bio:
Imran Ahmed (SM’21) is currently working as Assistant Professor at the Institute of Management Sciences, Pakistan. He received the Ph.D. degree with the computer science major from the University of Southampton, UK. He did his MS-IT from the Institute of Management Sciences, Pakistan, with major research in computer vision. He received the B.Sc. degree in computer science and mathematics from Edwardes College Peshawar, Pakistan, and the M.Sc. degree in computer science from the University of Peshawar, Pakistan. He has several research interests such as deep learning, machine learning, data science, computer vision, feature extraction, digital image and signal processing, medical image processing, bio-metrics, pattern recognition, and data mining. He has attended several national & international conferences in these areas. He has been acting as a Reviewer in journals such as IEEE Industrial Electronics, IEEE Access, Journal of Ambient Intelligence (Elsevier), etc

Sadia Din is currently working as an Assistant Professor in the Department of Information and Communication Engineering, Yeungnam University, South Korea. Previously, she was working as a Post-Doctoral Researcher in Kyungpook National University, South Korea (Mar. 2020–Aug. 2020). She received the Ph.D. degree in data science and master degree in computer science from Kyungpook National University, South Korea, and Abasyn University, Islamabad Pakistan, in 2020 and 2015, respectively. During the Ph.D., she was working on various projects including artificial learning, machine/deep learning, internet of things, and big data analytics. In 2015, she was Visiting Researcher at CCMP Lab, Kyungpook National University, South Korea, where she was working on big data and internet of things. Her area of research was big data, 5G, IOT and data science. She has published some highly reputed conferences such as IEEE LCN, ACM SAC, ICC, Globecom and some SCIE journal at the beginning of her research career. She was the chair for couple of sessions in IEEE LCN 2017 in Singapore. She was the Chair for the IEEE International Conf. on Local Computer Networks (LCN’18). She is serving as a Guest Editor injournal of Wiley

Gwanggil Jeon (SM’20) received the B.S., M.S., and Ph.D. (summa cum laude) degrees from the Department of Electronics and Computer Engineering, Hanyang University, Korea, in 2003, 2005, and 2008, respectively. From Sept. 2009 to Aug. 2011, he was with the School of Information Technology and Engineering, University of Ottawa, Canada, as a Post-Doctoral Fellow. From Sept. 2011 to Feb. 2012, he was with the Graduate School of Science and Technology, Niigata University, Japan, as an Assistant Professor. From Dec. 2014 to Feb. 2015 and Jun. 2015 to Jul. 2015, he was a Visiting Scholar at Centre de Mathématiques et Leurs Applications (CMLA), École Normale Supérieure Paris-Saclay (ENS-Cachan), France. From 2019 to 2020, he was a Prestigious Visiting Professor at Dipartimento di Informatica, Università degli Studi di Milano Statale, Italy. He is currently a Full Professor at Xidian University, China, and at Incheon National University, Korea. He was a Visiting Professor at Sichuan University, China, Universitat Pompeu Fabra, Spain, Xinjiang University, China, King Mongkut’s Institute of Technology Ladkrabang, Thailand, and University of Burgundy, France. Dr. Jeon is an Associate Editor of Sustainable Cities and Society, IEEE Access, Real-Time Image Processing, Journal of System Architecture, and MDPI Remote Sensing. Dr. Jeon was a recipient of the IEEE Chester Sall Award in 2007 and the ETRI Journal Paper Award in 2008

Francesco Piccialli (M’20) is currently Assistant Professor (tenure track) of computer science at the Department of Mathematics and Applications “R. Caccioppoli” (DMA) of the University of Naples Federico II (UNINA). He received the laurea degree (BSc+MSc) in computer science and the Ph.D. in computational and computer sciences from the University of Naples Federico II. He is the Founder and Scientific Director of the M.O.D.A.L. Research Group that is engaged in cutting-edge on novel methodologies, applications and services in data science and machine learning fields and their emerging application domains. He has been involved in research and development projects in the research areas of machine learning, deep learning, data science, internet of things. He is author of many papers (90+) in international conferences and top-level journals (IEEE, Springer, ACM, and Elsevier)

Giancarlo Fortino (SM’12) is a Full Professor of computer engineering at the Department of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received the Ph.D. degree in computer engineering from Unical in 2000. He is also Distinguished Professor at Wuhan University of Technology and Huazhong Agricultural University, High-End Expert at HUST, Senior Research Fellow at the ICAR-CNR Institute, and CAS PIFI Visiting Scientist at SIAT-Shenzhen. He is the Director of the SPEME lab at Unical as well as Co-Chair of Joint labs on IoT established between Unical and WUT and SMU and HZAU chinese universities, respectively. His research interests include agent-based computing, wireless (body) sensor networks, and IoT. He is author of 450+ papers in int’l journals, conferences and books. He is (Founding) Series Editor of IEEE Press Book Series on Human-Machine Systems and EiC of Springer Internet of Things series and AE of many int’l journals such as IEEE TAC, IEEE THMS, IEEE IoTJ, IEEE SJ, IEEE SMCM, IEEE OJEMB, IEEE OJCS, Information Fusion, JNCA, EAAI, etc. He organized as Chair many int’l workshops and conferences (100+), was involved in a huge number of int’l conferences/workshops (500+) as IPC Member, is/was Guest-Editor of many special issues (60+). He is Co-Founder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems. He is currently Member of the IEEE SMCS BoG and of the IEEE Press BoG, and Chair of the IEEE SMCS Italian Chapter
Corresponding author: Gwanggil Jeon, e-mail: gjeon@inu.ac.kr
Received Date: 2020-05-22
Revised Date: 2020-08-11
Accepted Date: 2020-08-28

Available Online: 2020-09-16

Abstract

Abstract

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry. It has been progressively utilized in numerous applications, particularly in intelligent surveillance systems. It allows the deployment of smart cameras or optical sensors with computer vision techniques, which may serve in several object detection and tracking tasks. These tasks have been considered challenging and high-level perceptual problems, frequently dominated by relative information about the environment, where main concerns such as occlusion, illumination, background, object deformation, and object class variations are commonplace. In order to show the importance of top view surveillance, a collaborative robotics framework has been presented. It can assist in the detection and tracking of multiple objects in top view surveillance. The framework consists of a smart robotic camera embedded with the visual processing unit. The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization. The detection models are further combined with different tracking algorithms, including GOTURN, MEDIANFLOW, TLD, KCF, MIL, and BOOSTING. These algorithms, along with detection models, help to track and predict the trajectories of detected objects. The pre-trained models are employed; therefore, the generalization performance is also investigated through testing the models on various sequences of top view data set. The detection models achieved maximum True Detection Rate 93% to 90% with a maximum 0.6% False Detection Rate. The tracking results of different algorithms are nearly identical, with tracking accuracy ranging from 90% to 94%. Furthermore, a discussion has been carried out on output results along with future guidelines.
- Collaborative robotics,
- deep learning,
- object detection and tracking,
- top view,
- video surveillance

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References(101)

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沈阳化工大学材料科学与工程学院沈阳 110142

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Collaborative surveillance framework is presented for multiple object tracking and detection.
Framework consists of a smart camera, visual processing unit, & deep learning models.
Generalization performance of detection models has been investigated for top view.
Object tracking is performed by combining detection models with tracking algorithms.
Comparison of six tracking algorithms, and detection models, have also been made.

Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning

doi: 10.1109/JAS.2020.1003453

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通讯作者: 陈斌, bchen63@163.com

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