Unmanned Aerial Vehicle Video-Based Target Tracking Algorithm Using Sparse Representation

Wan, Minjie; Gu, Guohua; Qian, Wei; Ren, Kan; Maldague, Xavier; Chen, Qian

doi:10.1109/jiot.2019.2930656

Cited by 41 publications

(17 citation statements)

References 43 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main advantage of data fusion is improving data authenticity or availability [4]. Although a wide variety of methods have been proposed [11], [12], long-term object tracking is still a challenging problem when dealing with occlusion, out-of-view, scale, and illumination variation. To address these challenges, Hu et al [5] proposed a robust visual object tracking method based on binocular vision.…”

Section: Related Workmentioning

confidence: 99%

Multi-Robot Sensor Fusion Target Tracking With Observation Constraints

et al. 2021

View full text Add to dashboard Cite

In Mobile Robotics, visual tracking is an extremely important sub-problem. Some solutions found to reduce the problems arising from partial and total occlusion are the use of multiple robots. In this work, we propose a three-dimensional space target tracking based on a constrained multi-robot visual data fusion on the occurrence of partial and total occlusion. To validate our approach we first implemented a non-cooperative visual tracking where only the data from a single robot is used. Then, a cooperative visual tracking was tested, where the data from a team of robots is fused using a particle filter. To evaluate both approaches, a visual tracking environment with partial and total occlusions was created where the tracking was performed by a team of robots. The result of the experiment shows that the non-cooperative approach presented a lower computational cost than the cooperative approach but the inferred trajectory was impaired by the occlusions, a fact that did not occur in the cooperative approach due to the data fusion.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-Robot Sensor Fusion Target Tracking With Observation Constraints

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Wan et al used sparse representation theory to propose a target tracking algorithm for locating pedestrians and vehicles captured by drones. Compared with other advanced trackers, the proposed tracker can achieve better performance in terms of accuracy and success rate [11]. e above studies have shown that various new technologies under AI can be applied to sports competitions, whether for individual athletes or competition venue management.…”

Section: Introductionmentioning

confidence: 98%

Video Analysis of Belt and Road Sports Events Based on Wireless Network and Artificial Intelligence Technology

Ling

Pan

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Nonprofessionals pay more and more attention to sports events, but in the process of live sports events, the semantics of professional terms are rich. And it is difficult for nonprofessionals to understand the meaning of the game. In order to improve the viewing experience of nonprofessionals, by intelligent target tracking technology, a system to capture athletes’ movement process is proposed. It is possible to analyze the types of athletes’ movements. In competitions or ball games, the system can predict the location of the ball or the player’s drop point. The four different tennis courts, the player’s motion capture, and the accuracy of the tennis landing prediction are further analyzed. The results show that when the athlete is facing the capture camera, the accuracy of motion recognition is high. In the absence of data on the playing field, the accuracy of the system for determining the position of the tennis ball fluctuates greatly. Therefore, this kind of system needs to have enough data to support it before it can be applied to fall point prediction. The proposed system provides a certain reference basis for the design of intelligent sports control facilities.

show abstract

“…Unmanned aerial vehicles (UAVs) have gradually spread from military to civilian use and can be used in different areas, such as climate detection [ 1 ], environmental research [ 2 ], intelligent transportation [ 3 ], and rescue and search operations [ 4 ], by embedding various devices. For UAVs, performing missions in complex urban environments is more difficult than open space missions.…”

Section: Introductionmentioning

confidence: 99%

Complex Environment Path Planning for Unmanned Aerial Vehicles

Zhang

Ji-wu

Yang

et al. 2021

Sensors

View full text Add to dashboard Cite

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT*. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

show abstract

Unmanned Aerial Vehicle Video-Based Target Tracking Algorithm Using Sparse Representation

Cited by 41 publications

References 43 publications

Multi-Robot Sensor Fusion Target Tracking With Observation Constraints

Multi-Robot Sensor Fusion Target Tracking With Observation Constraints

Video Analysis of Belt and Road Sports Events Based on Wireless Network and Artificial Intelligence Technology

Complex Environment Path Planning for Unmanned Aerial Vehicles

Contact Info

Product

Resources

About