Parallel implementation of color-based particle filter for object tracking in embedded systems

Nhat, Truong Thanh; Kim, Sang-Hoon

doi:10.1186/s13673-016-0082-1

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…In this approach, the UAV searches for its surveillance target by comparing the similarities between the images shot during its flight and a preregistered image of the surveillance target. 19 When the surveillance target has been located, the UAV monitors the target. This method is appropriate for cases in which the location of the surveillance target is not fixed.…”

Section: Image-based Surveillance Target Configurationmentioning

confidence: 99%

Emerging ICT UAV applications and services: Design of surveillance UAVs

Kwak

Park

Sung

2019

Int J Communication

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Summary An unmanned aerial vehicle (UAV) is an aircraft with no pilot. The autonomous flight technology of a UAV is combined with emerging Information and Communication Technology (ICT) applications and services to monitor diverse kinds of environments. UAVs are suitable for surveillance of targets because of their onboard cameras. It is possible to autonomously capture images of surveillance targets based on predefined paths. However, UAVs must generate a flight path for autonomous flight considering the surveillance targets. This paper proposes a UAV control process for surveillance taking into account various aspects of their operation. In particular, because flights of autonomous UAVs are based on analyzed past flight records, this paper first described surveillance using UAVs and then introduced the sequential processes for the generation of optimal UAV flight paths.

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Section: Image-based Surveillance Target Configurationmentioning

confidence: 99%

Emerging ICT UAV applications and services: Design of surveillance UAVs

Kwak

Park

Sung

2019

Int J Communication

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“…A method for shooting object based on an image previously captured by a micro UAS was previously developed [24][25][26]. Using the features of the object, it checks whether there is an object in the area being photographed by the camera mounted on the micro UAS.…”

Section: Autonomous Micro Uas For Survaillencementioning

confidence: 99%

End-To-End Controls Using K-Means Algorithm for 360-Degree Video Control Method on Omnidirectional Camera-Equipped Autonomous Micro Unmanned Aircraft Systems

Kwak¹,

Sung²

2019

Applied Sciences

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Micro unmanned aircraft systems (micro UAS)-related technical research is important because micro UAS has the advantage of being able to perform missions remotely. When an omnidirectional camera is mounted, it captures all surrounding areas of the micro UAS. Normal field of view (NFoV) refers to a view presented as an image to a user in a 360-degree video. The 360-degree video is controlled using an end-to-end controls method to automatically provide the user with NFoVs without the user controlling the 360-degree video. When using the end-to-end controls method that controls 360-degree video, if there are various signals that control the 360-degree video, the training of the deep learning model requires a considerable amount of training data. Therefore, there is a need for a method of autonomously determining the signals to reduce the number of signals for controlling the 360-degree video. This paper proposes a method to autonomously determine the output to be used for end-to-end control-based deep learning model to control 360-degree video for micro UAS controllers. The output of the deep learning model to control 360-degree video is automatically determined using the K-means algorithm. Using a trained deep learning model, the user is presented with NFoVs in a 360-degree video. The proposed method was experimentally verified by providing NFoVs wherein the signals that control the 360-degree video were set by the proposed method and by user definition. The results of training the convolution neural network (CNN) model using the signals to provide NFoVs were compared, and the proposed method provided NFoVs similar to NFoVs of existing user with 24.4% more similarity compared to a user-defined approach.

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“…In addition, Truong and Kim 11 proposed an object tracking method using a particle filter through a condensation algorithm to track object movement. They addressed the issue of high-complexity computation by applying parallel programming.…”

Section: Introductionmentioning

confidence: 99%

“…Lee et al 12 presented an object tracking algorithm similar to that proposed by Truong and Kim 11 using a small region. They proposed a “home alone fainting” detection system by combining a Kalman filter and a continuously adaptive mean shift (CAMshift) algorithm using thermal imaging cameras.…”

Section: Introductionmentioning

confidence: 99%

Flood-fill-based object segmentation and tracking for intelligent vehicles

Chu

Cho

Huang

et al. 2019

International Journal of Advanced Robotic Systems

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In this article, an application for object segmentation and tracking for intelligent vehicles is presented. The proposed object segmentation and tracking method is implemented by combining three stages in each frame. First, based on our previous research on a fast ground segmentation method, the present approach segments three-dimensional point clouds into ground and non-ground points. The ground segmentation is important for clustering each object in subsequent steps. From the non-ground parts, we continue to segment objects using a flood-fill algorithm in the second stage. Finally, object tracking is implemented to determine the same objects over time in the final stage. This stage is performed based on likelihood probability calculated using features of each object. Experimental results demonstrate that the proposed system shows effective, real-time performance.

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Parallel implementation of color-based particle filter for object tracking in embedded systems

Cited by 13 publications

References 18 publications

Emerging ICT UAV applications and services: Design of surveillance UAVs

Emerging ICT UAV applications and services: Design of surveillance UAVs

End-To-End Controls Using K-Means Algorithm for 360-Degree Video Control Method on Omnidirectional Camera-Equipped Autonomous Micro Unmanned Aircraft Systems

Flood-fill-based object segmentation and tracking for intelligent vehicles

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