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RISS 인기검색어
- 검색키워드
- 저자 : Grafika Jati (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Dynamic swarm particle for fast motion vehicle tracking
Jati, Grafika,Gunawan, Alexander Agung Santoso,Jatmiko, Wisnu Electronics and Telecommunications Research Instit 2020 ETRI Journal Vol.42 No.1
Nowadays, the broad availability of cameras and embedded systems makes the application of computer vision very promising as a supporting technology for intelligent transportation systems, particularly in the field of vehicle tracking. Although there are several existing trackers, the limitation of using low-cost cameras, besides the relatively low processing power in embedded systems, makes most of these trackers useless. For the tracker to work under those conditions, the video frame rate must be reduced to decrease the burden on computation. However, doing this will make the vehicle seem to move faster on the observer's side. This phenomenon is called the fast motion challenge. This paper proposes a tracker called dynamic swarm particle (DSP), which solves the challenge. The term particle refers to the particle filter, while the term swarm refers to particle swarm optimization (PSO). The fundamental concept of our method is to exploit the continuity of vehicle dynamic motions by creating dynamic models based on PSO. Based on the experiments, DSP achieves a precision of 0.896 and success rate of 0.755. These results are better than those obtained by several other benchmark trackers.
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Oriented object detection in satellite images using convolutional neural network based on ResNeXt
Asep Haryono,Grafika Jati,Wisnu Jatmiko 한국전자통신연구원 2024 ETRI Journal Vol.46 No.2
Most object detection methods use a horizontal bounding box that causes problems between adjacent objects with arbitrary directions, resulting in misaligned detection. Hence, the horizontal anchor should be replaced by a rotating anchor to determine oriented bounding boxes. A two-stage process of delineating a horizontal bounding box and then converting it into an oriented bounding box is inefficient. To improve detection, a box-boundary-aware vector can be estimated based on a convolutional neural network. Specifically, we propose a ResNeXt101 encoder to overcome the weaknesses of the conven-tional ResNet, which is less effective as the network depth and complexity increase. Owing to the cardinality of using a homogeneous design and multi-branch architecture with few hyperparameters, ResNeXt captures better information than ResNet. Experimental results demonstrate more accurate and faster oriented object detection of our proposal compared with a baseline, achieving a mean average precision of 89.41% and inference rate of 23.67 fps.
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Ario Yudo Husodo,Grafika Jati,Amarulla Octavian,Wisnu Jatmiko 한국통신학회 2020 ICT Express Vol.6 No.2
We propose a method for optimizing multiple-drone pursuers’ performance in handling attacks from Kamikaze multiple-drone evaders on a battlefield. The central aspect of this problem is to minimize damage produced by evaders towards a defended area guarded by multiple-pursuers. We propose a communication strategy among pursuers where each pursuer can communicate with each other to decide which evaders should be chased and immobilized by each pursuer. We simulate the proposed method in a dynamic 3D environment. The simulation results conclude that our proposed method performs better than the commonly used algorithm for solving this kind of problem.
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