Real-time target tracking system for person-following robot

Ren, Qimin; Zhao, Qingjie; Qi, Hui; Li, Lingrui

doi:10.1109/chicc.2016.7554324

Cited by 18 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several works [15][16][17][18][19][20] use the first approach to approximate the distance information by triangulation methods applied on two or more RGB views of the same scene. However, the most used visual sensors for person detection are RGB-D cameras [21][22][23][24][25][26][27][28][29][30][31][32] that are able to get both RGB images and depth maps by exploiting infrared light. Several methods employ sensor fusion techniques to merge information from different kinds of sensing systems.…”

Section: Person Followingmentioning

confidence: 99%

“…Focusing on vision-based methods, different strategies can be adopted to detect the person in the environment. Mi et al [26], Ren et al [25] and Chi et al [29] all adopted the Microsoft Kinect SDK that directly provides skeleton position. Satake et al [16,17] used manually designed templates to extract relevant features and find the target location.…”

Section: Person Followingmentioning

confidence: 99%

See 1 more Smart Citation

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

et al. 2020

View full text Add to dashboard Cite

The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications.

show abstract

Section: Person Followingmentioning

confidence: 99%

Section: Person Followingmentioning

confidence: 99%

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Li et al [11] used a mobile robot with a red, green, blue (RGB) image segmentation process based on shape and color recognition. In [12], depth images and an H-S Histogram in a Hue, Saturation, and Value (HSV) space were obtained using a red, green, blue, depth (RGB-D) camera for service robots. Vision-based tracking for mobile robots has been proposed using template matching to follow the person [13,14].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature

Algabri

Choi

2020

Sensors

View full text Add to dashboard Cite

Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.

show abstract

“…Visual object tracking is an important element of many applications such as person-following robots ( [6] [5] [31] [18]), self-driving cars ( [1] [7] [30] [4]), or surveillance cameras ( [9] [23] [41] [39]), etc. The performance of such systems critically depends on a reliable and efficient object tracking algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fast Visual Object Tracking using Ellipse Fitting for Rotated Bounding Boxes

Chen

Tsotsos

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

View full text Add to dashboard Cite

In this paper, we demonstrate a novel algorithm that uses ellipse fitting to estimate the bounding box rotation angle and size with the segmentation(mask) on the target for online and real-time visual object tracking. Our method, SiamMask E, improves the bounding box fitting procedure of the state-of-the-art object tracking algorithm SiamMask and still retains a fast-tracking frame rate (80 fps) on a system equipped with GPU (GeForce GTX 1080 Ti or higher). We tested our approach on the visual object tracking datasets (VOT2016, VOT2018, and VOT2019) that were labeled with rotated bounding boxes. By comparing with the original SiamMask, we achieved an improved Accuracy of 65.2% and 30.9% EAO on VOT2019, which is 5.6% and 2.6% higher than the original SiamMask.The implementation is available on GitHub: https://github.com/ baoxinchen/siammask_e.

show abstract

Real-time target tracking system for person-following robot

Cited by 18 publications

References 13 publications

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature

Fast Visual Object Tracking using Ellipse Fitting for Rotated Bounding Boxes

Contact Info

Product

Resources

About