Visual object tracking

University of Technology Sydney. Faculty of Engineering and Information Technology.Visual object tracking is a critical task in many computer-vision-related applications, such as surveillance and robotics. If the tracking target is provided in the first frame of a video, the tracker will predict the location and the shape of the target in the following frames. Despite the significant research effort that has been dedicated to this area for several years, this field remains challenging due to a number of issues, such as occlusion, shape variation and drifting, all of which adversely affect the performance of a tracking algorithm. This research focuses on incorporating the spatial and temporal context to tackle the challenging issues related to developing robust trackers. The spatial context is what surrounds a given object and the temporal context is what has been observed in the recent past at the same location. In particular, by considering the relationship between the target and its surroundings, the spatial context information helps the tracker to better distinguish the target from the background, especially when it suffers from scale change, shape variation, occlusion, and background clutter. Meanwhile, the temporal contextual cues are beneficial for building a stable appearance representation for the target, which enables the tracker to be robust against occlusion and drifting. In this regard, we attempt to develop effective methods that take advantage of the spatial and temporal context to improve the tracking algorithms. Our proposed methods can benefit three kinds of mainstream tracking frameworks, namely the template-based generative tracking framework, the pixel-wise tracking framework and the tracking-by-detection framework. For the template-based generative tracking framework, a novel template based tracker is proposed that enhances the existing appearance model of the target by introducing mask templates. In particular, mask templates store the temporal context represented by the frame difference in various time scales, and other templates encode the spatial context. Then, using pixel-wise analytic tools which provide richer details, which naturally accommodates tracking tasks, a finer and more accurate tracker is proposed. It makes use of two convolutional neural networks to capture both the spatial and temporal context. Lastly, for a visual tracker with a tracking-by-detection strategy, we propose an effective and efficient module that can improve the quality of the candidate windows sampled to identify the target. By utilizing the context around the object, our proposed module is able to refine the location and dimension of each candidate window, thus helping the tracker better focus on the target object

Evidence for Dirac Fermions in a honeycomb lattice based on silicon

Silicene, a sheet of silicon atoms in a honeycomb lattice, was proposed to be a new Dirac-type electron system similar as graphene. We performed scanning tunneling microscopy and spectroscopy studies on the atomic and electronic properties of silicene on Ag(111). An unexpected $\sqrt{3}\times \sqrt{3}$ reconstruction was found, which is explained by an extra-buckling model. Pronounced quasi-particle interferences (QPI) patterns, originating from both the intervalley and intravalley scattering, were observed. From the QPI patterns we derived a linear energy-momentum dispersion and a large Fermi velocity, which prove the existence of Dirac Fermions in silicene.Comment: 6 pages, 4 figure

Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength represented by Antenna Gain Array Manifold

Inspection robots are widely used in the field of smart grid monitoring in substations, and partial discharge (PD) is an important sign of the insulation state of equipments. PD direction of arrival (DOA) algorithms using conventional beamforming and time difference of arrival (TDOA) require large-scale antenna arrays and high computational complexity, which make them difficult to implement on inspection robots. To address this problem, a novel directional multiple signal classification (Dir-MUSIC) algorithm for PD direction finding based on signal strength is proposed, and a miniaturized directional spiral antenna circular array is designed in this paper. First, the Dir-MUSIC algorithm is derived based on the array manifold characteristics. This method uses strength intensity information rather than the TDOA information, which could reduce the computational difficulty and the requirement of array size. Second, the effects of signal-to-noise ratio (SNR) and array manifold error on the performance of the algorithm are discussed through simulations in detail. Then according to the positioning requirements, the antenna array and its arrangement are developed, optimized, and simulation results suggested that the algorithm has reliable direction-finding performance in the form of 6 elements. Finally, the effectiveness of the algorithm is tested by using the designed spiral circular array in real scenarios. The experimental results show that the PD direction-finding error is 3.39{\deg}, which can meet the need for Partial discharge DOA estimation using inspection robots in substations.Comment: 8 pages,13 figures,24 reference

SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion

Most existing learning-based infrared and visible image fusion (IVIF) methods exhibit massive redundant information in the fusion images, i.e., yielding edge-blurring effect or unrecognizable for object detectors. To alleviate these issues, we propose a semantic structure-preserving approach for IVIF, namely SSPFusion. At first, we design a Structural Feature Extractor (SFE) to extract the structural features of infrared and visible images. Then, we introduce a multi-scale Structure-Preserving Fusion (SPF) module to fuse the structural features of infrared and visible images, while maintaining the consistency of semantic structures between the fusion and source images. Owing to these two effective modules, our method is able to generate high-quality fusion images from pairs of infrared and visible images, which can boost the performance of downstream computer-vision tasks. Experimental results on three benchmarks demonstrate that our method outperforms eight state-of-the-art image fusion methods in terms of both qualitative and quantitative evaluations. The code for our method, along with additional comparison results, will be made available at: https://github.com/QiaoYang-CV/SSPFUSION.Comment: Submitted to IEE