A particle filter object tracking based on feature and location fusion

Tian, Peng

doi:10.1109/icsess.2015.7339168

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Pada penelitian sebelumnya [2], [16], [17], teknologi ini menerapkan metode partikel filter sebagai sistem tracking-nya. Partikel filter adalah algoritma deteksi objek yang nilainya bergantung pada representasi sampel atau partikel yang didistribusikan secara acak [16].…”

Section: Pendahuluanunclassified

“…Partikel filter adalah algoritma deteksi objek yang nilainya bergantung pada representasi sampel atau partikel yang didistribusikan secara acak [16]. Namun, pada hasil panelitian [2], [17] menunjukan bahwa deteksi marker tidak mendeteksi secara maksimal karena masih terdapat banyak noise pada hasilnya. Sedangkan, pada penelitian [16] menunjukan waktu deteksi objek yang dibutuhkan cukup besar, yaitu 4,98 detik.…”

Section: Pendahuluanunclassified

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Investigasi Pengaruh Step Training pada Metode Single Shot Multibox Detector untuk Marker dalam Teknologi Augmented Reality

Sutama

Wibowo

Rahmania

2020

FIFO

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Nowadays, Artificial Intelligence is one of the most developing technology, especially on Augmented Reality (AR). AR is a technology which connected between real world and virtual in a real time that allows user to interact directly and display it in 3D. AR technology has two methods, that are AR based on marker and AR based on markerless. However, AR based on marker need an object detection system which has high performance as an interaction tools between user and the device. Single shot multibox detector (SSD) is an object detection algorithm that has fast learning computation and good performance. This method is affected by some parameters like number of epoch, learning rate, batch size, step training, etc. However, to create a good system it took a long process such as taking dataset, labelling process, then training and testing models to gain the best performance. In this experiment, we analyze SSD method in AR technology using inception architecture as pre-trained Convolutional neural network (CNN), and then do transfer learning to minimize amount training time. The configuration that used is the number of step training. The result of this experiment gets the best accuracy in 70.17%. Then, the best performance is used as an object detection model for marker’s AR technology.Abstrak Saat ini, Artificial intelligence merupakan teknologi yang sedang berkembang pesat. Salah satunya adalah teknologi Augmented Reality (AR). AR adalah teknologi yang menggabungkan dunia nyata dengan virtual secara real-time dengan interaksi pengguna secara langsung dan menampilkannya dalam bentuk 3D. Teknologi AR ini memiliki dua metode yaitu dengan marker dan markerless. Dalam perkembangannya, AR berbasis marker membutuhkan sistem deteksi objek yang memiliki performa tinggi sebagai alat interaksi antara pengguna dengan perangkatnya. Single shot multibox detector (SSD) merupakan algoritma deteksi objek yang memiliki komputasi pembelajaran dan kinerja yang baik. Metode ini dipengaruhi oleh beberapa parameter seperti jumlah lapisan konvolusi, epoch, learning rate, jumlah batch, step training, dll. Namun, dalam mengimplementasikannya diperlukan proses yang cukup panjang seperti, pengambilan dataset, proses pelabelan, proses pelatihan menggunakan metode SSD, dan melakukan pengujian terhadap beberapa model untuk mencari perfomansi paling baik. Dalam percobaan ini, kami melakukan analisis terhadap metode SSD pada teknologi AR menggunakan arsitektur Inception sebagai pre-trained Convolutional neural network (CNN), kemudian dilakukan transfer learning untuk memperkecil jumlah kelas data pelatihan dan waktu pelatihan data. Konfigurasi yang digunakan berupa jumlah step pada pelatihan. Hasil dari penilitian ini menunjukan akurasi terbaik sebesar 70,17%. Kemudian, perfomansi terbaik digunakan sebagai model deteksi objek untuk marker pada teknologi AR.

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Section: Pendahuluanunclassified

Investigasi Pengaruh Step Training pada Metode Single Shot Multibox Detector untuk Marker dalam Teknologi Augmented Reality

Sutama

Wibowo

Rahmania

2020

FIFO

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“…Zhao et al [27] fused the color feature and the Haar feature to overcome the challenges of illumination and pose change, but the algorithm could not handle partial occlusion. Tian [28] represented the target by the multiple feature descriptions, based on the selected color subspace, to improve the robustness of the target tracking to some extent. However, the algorithm was not able to handle the occlusion trouble.…”

Section: Methodsmentioning

confidence: 99%

Target Tracking Algorithm Based on an Adaptive Feature and Particle Filter

Lin

Huang

2018

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Abstract:To boost the robustness of the traditional particle-filter-based tracking algorithm under complex scenes and to tackle the drift problem that is caused by the fast moving target, an improved particle-filter-based tracking algorithm is proposed. Firstly, all of the particles are divided into two parts and put separately. The number of particles that are put for the first time is large enough to ensure that the number of the particles that can cover the target is as many as possible, and then the second part of the particles are put at the location of the particle with the highest similarity to the template in the particles that are first put, to improve the tracking accuracy. Secondly, in order to obtain a sparser solution, a novel minimization model for an L p tracker is proposed. Finally, an adaptive multi-feature fusion strategy is proposed, to deal with more complex scenes. The experimental results demonstrate that the proposed algorithm can not only improve the tracking robustness, but can also enhance the tracking accuracy in the case of complex scenes. In addition, our tracker can get better accuracy and robustness than several state-of-the-art trackers.

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