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Towards Collaborative Robotics in Top View Surveillance:A Framework for Multiple object tracking by detection Using Deep Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1253-1270页

作者： Imran Ahmed Sadia Din Gwanggil Jeon Francesco Piccialli Giancarlo Fortino the Center of excellence in Information Technology Institute of Management SciencesPeshawar 25000Pakistan the Department of Information and Communication Engineering Yeungnam UniversitySouth Korea the School of Electronic Engineering Xidian UniversityXi’an 710071China the Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea the Department of Mathematics and Applications“R.Caccioppoli” University of Naples Federico IINapoli 80138Italy the Department of Informatics ModelingElectronics and SystemsUniversity of CalabriaRendeCS 87036Italy

Collaborative Robotics is one of the high-interest research topics in the area of academia and *** has been progressively utilized in numerous applications,particularly in intelligent surveillance *** allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking *** 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 *** order to show the importance of top view surveillance,a collaborative robotics framework has been *** can assist in the detection and tracking of multiple objects in top view *** framework consists of a smart robotic camera embedded with the visual processing *** existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and *** detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and *** algorithms,along with detection models,help to track and predict the trajectories of detected *** pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data *** detection models achieved maximum True detection Rate 93%to 90%with a maximum 0.6%False detection *** 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.

关键词： Collaborative robotics deep learning object detection and tracking top view video surveillance

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