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Convolutional neural network (CNN) has drawn increasing interest in visual tracking, among which fully-convolutional Siamese network based method (SiamFC) is quite popular due to its competitive performance in both precision and efficiency. Generally, SiamFC captures robust semantics from high-level features in the last layer but ignores detailed spatial features in earlier layers, thus tending to...
Object deformation and occlusion are ubiquitous problems for visual tracking. Though many efforts have been made to handle object deformation and occlusion, most existing tracking algorithms fail in case of large deformation and severe occlusion. In this paper, we propose a graph learning-based tracking framework to handle both challenges. For each consecutive frame pair, we construct a weighted graph,...
To achieve the goal of intelligent motion perception effort has been spent on visual object tracking, which is one of the most important research topics in computer vision. Visual video tracking includes object detection and tracking which are closely related process. Object detection involves verifying the presence of object and object tracking is monitoring object's spatial and temporal changes...
Multi-object model-free tracking is challenging because the tracker is not aware of the objects' type (not allowed to use object detectors), and needs to distinguish one object from background as well as other similar objects. Most existing methods keep updating their appearance model individually for each target, and their performance is hampered by sudden appearance change and/or occlusion. We propose...
Due to the superiority in handling label ambiguity, multiple instance learning (MIL) has been introduced into adaptive tracking-by-detection methods to alleviate drift and yields promising tracking performance. However, the MIL tracker assumes that all samples in a positive bag contribute equally to the bag probability, which ignores sample importance. To address this issue, in this paper we propose...
Robust visual object tracking against occlusions and deformations is still very challenging task. To tackle these issues, existing Convolutional Neural Networks (CNNs) based trackers either fail to handle them or can just run in low speed. In this paper, we present a realtime tracker which is robust to occlusions and deformations based on a Region-based, Multi-Scale Fully Convolutional Siamese Network...
Computer Vision and Machine Learning are the key to develop autonomous robots. While engaged with a IEEE Open Challenge, in which the robots need to recognize a miniature of a cow, we saw a solution in these areas. The main contribution of this paper is the algorithm implemented to identify and follow a known object, the miniature of a cow. We are constructing an application based on Image Processing...
Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable...
Convolutional neural network (CNN) based trackers have achieved significant performances in tracking recently. Most existing CNN-based trackers regard tracking as a classification or similarity searching problem. The two methods have their respective superiorities and limitations because of different supervised objectives. In this paper, we propose a multi-task CNN for visual tracking, not only fully...
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from the background on the basis of the pixel-level similarity between two object units. The proposed network represents a target object using features from different depth layers in order to take advantage of both the spatial...
Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - on the fly - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the target is not modeled over time which...
Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. They only need a small set of training samples from the initial frame to generate an appearance model. However, existing DCFs learn the filters separately from feature extraction, and update these filters using a moving average operation with an empirical weight. These DCF trackers hardly benefit from...
Visual object tracking is a fundamental and time-critical vision task. Recent years have seen many shallow tracking methods based on real-time pixel-based correlation filters, as well as deep methods that have top performance but need a high-end GPU. In this paper, we learn to improve the speed of deep trackers without losing accuracy. Our fundamental insight is to take an adaptive approach, where...
This work presents an ensemble-based visual object tracker called KFebT. This method can fuse using a Kalman Filter the result of several out-of-the box trackers or specialist methods that solve parts of the problem, like methods that only estimate the target scale variation. Our purpose in joining multiple trackers is to take advantage of the different strengths and weaknesses of each approach. The...
Visual tracking is a challenging task due to a number of factors, such as occlusions, deformations, illumination variations and abrupt motion changes present in a video sequence. Generally, trackers are robust to some of these factors, but do not achieve satisfactory results when dealing with multiple factors at the same time. More robust results when multiple factors are present can be obtained by...
This paper presents a novel solution to the occlusion handling problem in pedestrian tracking using labeled random finite set theory. The occlusion handling module uses motion and color cues of tracked targets to recover target labels after occlusion. An effective algorithm is also proposed for false alarm detection and removal which is designed based on tracked targets features such as, overlap ratio,...
In this paper, we experimentally examine the relationship between visual cognition difficulty and target-tracking eye movements, which recorded during moving target cognition. Generally, such eye movements are observed when humans perceive a moving object and they vary widely due to many factors, such as target shape, backgrounds, illumination conditions, and so on. Several systems have been proposed...
In this paper, a robust visual tracking system with occlusion handling is proposed to track the target with real-time performance. The thermal camera, which can observe the heat originated from the target such as the human body or vehicle, can collaborate with the color camera to track the target in the cluttered environment or under occlusion. Unlike the general tracking by using the color camera...
In this paper, a novel approach to online multi-object tracking is proposed via Labeled Random Finite Sets (RFS) combined with appearance learning. The Labeled RFS formulation of the multi-object state naturally accommodates a time-varying number of objects, track labels, and false positive rejection in a single Bayesian framework. The proposed algorithm exploits appearance feature information for...
Recently deep neural networks have been widely employed to deal with the visual tracking problem. In this work, we present a new deep architecture which incorporates the temporal and spatial information to boost the tracking performance. Our deep architecture contains three networks, a Feature Net, a Temporal Net, and a Spatial Net. The Feature Net extracts general feature representations of the target...
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