Visual Tracking Based on Complementary Learners with Distractor Handling

Abstract

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.

Convolutional Shallow Features for Performance Improvement of Histogram of Oriented Gradients in Visual Object Tracking

“…IVT [24], kcf mtsa [19], KCF2 [19], kcfdp [19], kcfv2 [19], L1APG [9], LGT [25], loft lite [19], LT FLO [26], LPMT [15], matflow [19], MCT [19], MEEM [27], MIL [4], mkcf plus [19], muster [28], mvcft [19], ncc [29], OAB [19], OACF [19], PKLTF [19], rajssc [19], RobStruck [19], s3Tracker [19], samf [30], SCBT [31], sKCF [19], sme [19], SODLT [32], srat [19], STC [33], struck [34], sumshift [35], TGPR [36], tric [19], and zhang [19]. Further, to prove the advantages of the proposed method, a comparison among the proposed method, the proposed method using only CS features, and the proposed method using only HOG features was also conducted.…”

“…In their method, they focused on how to handle the problem of a change in size during visual tracking. Other features for correlation-filter-based visual tracking, such as adaptive color features, were used in [14], and recently a fusion between color histogram features and HOG features with distractor handling was proposed in [15]. Unfortunately, these works have a limitation in that they use only a one-dimensional (1D) feature map.…”

Investigasi pengaruh Step Training pada Skema Same-Padding untuk Metode Faster R-CNN dalam Teknologi Augmented Reality

“…Hasil konvolusi pada citra dapat berbedabeda tergantung dengan padding dan stride yang digunakan. Fitur dari CNN juga dapat meningkatkan visual tracking [11] [12]. Operasi konvolusi dapat dilihat pada Gambar 2.…”

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

“…Metode-metode yang dapat digunakan untuk deteksi objek antara lain Region-based Convolutional neural network (RCNN), Faster RCNN [3], You Only Look Once (YOLO) [4], Single shot multibox detector (SSD) [5], dan sebagainya. Deteksi objek dapat diimplementasikan pada berbagai aplikasi di kehidupan, misalnya video surveillance, face recognition, maupun visual tracking [7], [8], [9].…”