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Edge Deployment of Vision-Based Model for Human Following Robot 23

Edge Deployment of Vision-Based Model for Human Following Ro...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Manzoor, Sumaira Kim, Eun-Jin Bae, Sang-Hyeon Kuc, Tae-Yong Creative Algorithms and Sensor Evolution Laboratory Suwon85761 Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon2066 Korea Republic of

ISBN: (纸本)9788993215274

Mobile robots are proliferating at a significant pace and the continuous interaction between humans and robots opens the doors to facilitate our daily life activities. Following the target person with the robot is an important human-robot interaction (HRI) task that leads to its applications in industrial, domestic, and medical assistant robots. To implement the robotic tasks, traditional solutions rely on cloud servers that cause significant communication overhead due to data offloading. In our work, we overcome this potential issue of cloud-based solutions, by implementing the task of a hum-following robot (HFR) at the Nvidia Jetson Xavier NX edge platform. To perform the HFR task, typical approaches track the target person only from behind. While, our work allows the robot to track the person from behind, front, and side views (left & right). In this article, we combine the latest advances of deep learning and metric learning by presenting two trackers: Single Person Head Detection-based Tracking (SPHDT) model and Single Person full-Body Detection-based Tracking (SPBDT) model. For both models, we leverage a deep learning-based single object detector called MobileNetSSD with a metric learning-based re-identification model, DaSiamRPN. We perform the qualitative analysis considering six major environmental factors: pose change, illumination variations, partial occlusion, full occlusion, wall corner, and different viewing angles. Based on the better performance of SPBDT, compared to SPHDT in the experimental results, we select SPBDT model for the robot to track the target. We also use this vision model to provide the relative position, location, distance, and angle of the target person to control the robot's movement for performing the human-following task. © 2023 ICROS.

关键词： Object recognition

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Edge Deployment of Vision-Based Model for Human Following Robot

Edge Deployment of Vision-Based Model for Human Following Ro...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Sumaira Manzoor Eun-jin Kim Sang-Hyeon Bae Tae-Yong Kuc Creative Algorithms and Sensor Evolution Laboratory Suwon Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

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Qualitative Analysis of Single Object and Multi Object Tracking Models

Qualitative Analysis of Single Object and Multi Object Track...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Sumaira Manzoor Kyu-Hyun Sung Yueyuan Zhang Ye-Chan An Tae-Yong Kuc Creative Algorithms and Sensor Evolution Laboratory Suwon Republic of Korea Department of Electrical and Computer Engineering College of Information and Communication Engineering Sungkyunkwan University Suwon Korea

Tracking the object(s) of interest in the real world is one of the most salient research areas that has gained widespread attention due to its applications. Although different approaches based on traditional machine learning and modern deep learning have been proposed to tackle the single and multi-object tracking problems, these tasks are still challenging to perform. In our work, we conduct a comparative analysis of eleven object trackers to determine the most robust single object tracker (SOT) and multi-object tracker (MOT). The main contributions of our work are (1) employing nine pre-trained tracking algorithms to carry out the analysis for SOT that include: SiamMask, GOTURN, BOOSTING, MIL, KCF, TLD, MedianFlow, MOSSE, CSRT; (2) investigating MOT by integrating object detection models with object trackers using YOLOv4 combined with DeepSort, and CenterNet coupled with SORT; (3) creating our own testing videos dataset to perform experiments; (4) performing the qualitative analysis based on the visual representation of results by considering nine significant factors that are appearance and illumination variations, speed, accuracy, scale, partial and full-occlusion, report failure, and fast motion. Experimental results demonstrate that SiamMask tracker overcomes most of the environmental challenges for SOT while YOLOv+DeepSort tracker obtains good performance for MOT. However, these trackers are not robust enough to handle full occlusion in real-world scenarios and there is always a trade-off between tracking accuracy and speed.

关键词： Deep learning Analytical models Visualization Lighting Object detection Control systems Object tracking

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