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

• 作者: Imran Ahmed Sadia Din Gwanggil Jeon Francesco Piccialli Giancarlo Fortino
• 来源期刊: 自动化学报（英文版）
• 年，卷（期）: 2021,（7）
• 所属期刊栏目: SPECIAL ISSUE ON COGNITIVE COMPUTING FOR COLLABORATIVE ROBOTICS
• 页码范围: 1253-1270
• 页数: 18页
• 语种: 英文

摘要

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.