FPGA-based real-time moving object detection for walking robots

Laika, Andreas; Paul, Johny; Claus, Christopher; Stechele, Walter; Auf, Adam El Sayed; Maehle, Erik

doi:10.1109/ssrr.2010.5981554

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Detection technique and application [6][7][8][9][10][11] Static camera (i) Background subtraction (ii) Using GMM, ViBE, and so forth [23] Moving robot (i) Detecting moving object using (ii) Optical flow and frame differencing [24] UAV (i) Detecting and tracking object feature [ to detect moving vehicle in aerial videos. The research in [16] presented two different approaches to detect and track moving vehicle and person using a Hierarchy of Gradient (HoG) based classifier.…”

Section: Camera Platformmentioning

confidence: 99%

“…They utilized background subtraction techniques such as Gaussian Mixture Model (GMM) and ViBE (Visual Background Extractor) to perform foreground object segmentation in static background video. The work in [23] has proposed FPGA-based moving object detection for a walking robot. They implemented ego-motion estimation using optical flow technique and frame differencing in hardware/software codesign system.…”

Section: Camera Platformmentioning

confidence: 99%

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FPGA-Based Real-Time Moving Target Detection System for Unmanned Aerial Vehicle Application

Tang

Shaikh-Husin

Sheikh

et al. 2016

International Journal of Reconfigurable Computing

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Moving target detection is the most common task for Unmanned Aerial Vehicle (UAV) to find and track object of interest from a bird’s eye view in mobile aerial surveillance for civilian applications such as search and rescue operation. The complex detection algorithm can be implemented in a real-time embedded system using Field Programmable Gate Array (FPGA). This paper presents the development of real-time moving target detection System-on-Chip (SoC) using FPGA for deployment on a UAV. The detection algorithm utilizes area-based image registration technique which includes motion estimation and object segmentation processes. The moving target detection system has been prototyped on a low-cost Terasic DE2-115 board mounted with TRDB-D5M camera. The system consists of Nios II processor and stream-oriented dedicated hardware accelerators running at 100 MHz clock rate, achieving 30-frame per second processing speed for 640 × 480 pixels’ resolution greyscale videos.

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Section: Camera Platformmentioning

confidence: 99%

Section: Camera Platformmentioning

confidence: 99%

FPGA-Based Real-Time Moving Target Detection System for Unmanned Aerial Vehicle Application

Tang

Shaikh-Husin

Sheikh

et al. 2016

International Journal of Reconfigurable Computing

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“…Research in egomotion estimation has mainly focused on ground robots [35], [36], [41], for which the application requirements differ significantly from the case where the egomotion estimation of a UAV is targeted. Namely, for the UAV case, the problem can be simplified as it can be safely assumed that the detected feature points are positioned on the same plane relative to the camera due to the altitude of the UAV.…”

Section: E Comparison To Existing Workmentioning

confidence: 99%

“…The resulting system consumes around 80% of the available resources achieving a maximum of 28 frames/sec, while using a camera with 320 × 256 pixels resolution. In [41], the egomotion estimation of a walking robot is performed as part of a bigger system. The employed motion model limits the degrees of freedom to four and has thus limitations in differentiating between rotation and translation in the x and y axis.…”

Section: E Comparison To Existing Workmentioning

confidence: 99%

Robust Real-Time Super-Resolution on FPGA and an Application to Video Enhancement

Angelopoulou

Bouganis

Cheung

et al. 2009

ACM Trans. Reconfigurable Technol. Syst.

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Abstract-The use of Unmanned Aerial Vehicles (UAVs) in commercial and warfare activities has intensified over the last decade. One of the main challenges is to enable UAVs to become as autonomous as possible. A vital component towards this direction is the robust and accurate estimation of the egomotion of the UAV. Egomotion estimation can be enhanced by equipping the UAV with a video camera, which enables a visionbased egomotion estimation. However, the high computational requirements of vision-based egomotion algorithms, combined with the real-time performance and low power consumption requirements that are related to such an application, cannot be met by general-purpose processing units. This work presents a system architecture that employs a Field Programmable Gate Array (FPGA) as the main processing platform connected to a low-power CPU that targets the problem of vision-based egomotion estimation in a UAV. The performance evaluation of the proposed system, using real data captured by a UAV's onboard camera, demonstrates the ability of the system to render accurate estimation of the egomotion parameters, meeting at the same time the real-time requirements imposed by the application.

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Implementation of Shortest path planning algorithm without track using FPGA robot: A new approach

Chinnaiah¹,

Vani²,

Satyasavithri

et al. 2014

2014 International Conference on Advances in Electrical Engineering (ICAEE)

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FPGA-based real-time moving object detection for walking robots

Cited by 7 publications

References 9 publications

FPGA-Based Real-Time Moving Target Detection System for Unmanned Aerial Vehicle Application

FPGA-Based Real-Time Moving Target Detection System for Unmanned Aerial Vehicle Application

Robust Real-Time Super-Resolution on FPGA and an Application to Video Enhancement

Implementation of Shortest path planning algorithm without track using FPGA robot: A new approach

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