The representation of the object is an important factor in building a robust visual object tracking algorithm. To resolve this problem, complementary learners that use color histogram-and correlation filter-based representation to represent the target object can be used since they each have advantages that can be exploited to compensate the other's drawback in visual tracking. Further, a tracking algorithm can fail because of the distractor, even when complementary learners have been implemented for the target object representation. In this study, we show that, in order to handle the distractor, first the distractor must be detected by learning the responses from the color-histogram-and correlation-filter-based representation. Then, to determine the target location, we can decide whether the responses from each representation should be merged or only the response from the correlation filter should be used. This decision depends on the result obtained from the distractor detection process. Experiments were performed on the widely used VOT2014 and VOT2015 benchmark datasets. It was verified that our proposed method performs favorably as compared with several state-of-the-art visual tracking algorithms.
Kemajuan teknologi informasi yang cepat dan tak terkontrol membuat berbagai konten negatif seperti pornografi dapat dengan mudah diakses. Konten pornografi terbukti dapat menyebabkan berbagai permasalahan terutama pada generasi muda. Beberapa pengembangan metode untuk pendeteksian pornografi telah dilakukan namun masih terkendala pada keterbatasan karakteristik data masukan. Pada penelitian kali ini dikembangkan sistem pendeteksi konten pornografi berbasis klasifikasi menggunakan Convolutional Neural Network (CNN) dengan arsitektur ResNet-50 untuk mengatasi permasalahan sebelumnya. Dalam proses perancangan model sistem, diterapkan berbagai konfigurasi epoch dan didapatkan bahwa performa dari sistem memiliki kecenderungan untuk meningkat seiring dengan pertambahan epoch. Performa terbaik diraih oleh sistem pada konfigurasi epoch 60 dengan akurasi 91,033%.
This paper presents the results of three-dimensional face point cloud smoothing based on a modified anisotropic diffusion method. The focus of this research was to obtain a 3D face point cloud with a smooth texture and number of vertices equal to the number of vertices input during the smoothing process. Different from other methods, such as using a template D face model, modified anisotropic diffusion only uses basic concepts of convolution and filtering which do not require a complex process. In this research, we used 6D point cloud face data where the first 3D point cloud contained data pertaining to noisy x-, y-, and z-coordinate information, and the other 3D point cloud contained data regarding the red, green, and blue pixel layers as an input system. We used vertex selection to modify the original anisotropic diffusion. The results show that our method has improved performance relative to the original anisotropic diffusion method.
Histogram of oriented gradients (HOG) is a feature descriptor typically used for object detection. For object tracking, this feature has certain drawbacks when the target object is influenced by a change in motion or size. In this paper, the use of convolutional shallow features is proposed to improve the performance of HOG feature-based object tracking. Because the proposed method works based on a correlation filter, the response maps for each feature are summed in order to obtain the final response map. The location of the target object is then predicted based on the maximum value of the optimized final response map. Further, a model update is used to overcome the change in appearance of the target object during tracking. A performance evaluation of the proposed method is obtained by using Visual Object Tracking 2015 (VOT2015) benchmark dataset and its protocols. The results are then provided based on their accuracy-robustness (AR) rank. Furthermore, through a comparison with several state-of-the-art tracking algorithms, the proposed method was shown to achieve the highest rank in terms of accuracy and a third rank for robustness. In addition, the proposed method significantly improves the robustness of HOG-based features.
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