Qiao Liu

Qiao Liu
Harbin Institute of Technology Shenzhen Graduate School | HITSZ · Computer Science and Technology

Doctor of Engineering

About

39
Publications
15,073
Reads
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928
Citations
Citations since 2016
39 Research Items
927 Citations
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2016201720182019202020212022050100150200250300
2016201720182019202020212022050100150200250300
2016201720182019202020212022050100150200250300

Publications

Publications (39)
Article
Thermal infrared (TIR) target tracking is susceptible to occlusion and similarity interference, which obviously affects the tracking results. To resolve this problem, we develop an Aligned Spatial-Temporal Memory network-based Tracking method (ASTMT) for the TIR target tracking task. Specifically, we model the scene information in the TIR target tr...
Article
Full-text available
When dealing with complex thermal infrared (TIR) tracking scenarios, the single category feature is not sufficient to portray the appearance of the target, which drastically affects the accuracy of the TIR target tracking method. In order to address these problems, we propose an adaptively multi-feature fusion model (AMFT) for the TIR tracking task...
Preprint
Existing methods for video-based person re-identification (ReID) mainly learn the appearance feature of a given pedestrian via a feature extractor and a feature aggregator. However, the appearance models would fail when different pedestrians have similar appearances. Considering that different pedestrians have different walking postures and body pr...
Article
Triple loss is widely used to detect learned descriptors and achieves promising performance. However, triple loss fails to fully consider the influence of adjacent descriptors from the same type of sample, which is one of the main reasons for image mismatching. To solve this problem, we propose a descriptor network based on triple loss with a simil...
Article
Thermal InfraRed (TIR) target trackers are easy to be interfered by similar objects, while susceptible to the influence of the target occlusion. To solve these problems, we propose a structural target-aware model (STAMT) for the thermal infrared target tracking tasks. Specifically, the proposed STAMT tracker can learn a target-aware model, which ca...
Article
Full-text available
Most existing trackers are based on using a classifier and multi-scale estimation to estimate the target state. Consequently, and as expected, trackers have become more stable while tracking accuracy has stagnated. While trackers adopt a maximum overlap method based on an intersection-over-union (IoU) loss to mitigate this problem, there are defect...
Article
Full-text available
The feature models used by existing Thermal InfraRed (TIR) tracking methods are usually learned from RGB images due to the lack of a large-scale TIR image training dataset. However, these feature models are less effective in representing TIR objects and they are difficult to effectively distinguish distractors because they do not contain fine-grain...
Preprint
Full-text available
Along with the rapid progress of visual tracking, existing benchmarks become less informative due to redundancy of samples and weak discrimination between current trackers, making evaluations on all datasets extremely time-consuming. Thus, a small and informative benchmark, which covers all typical challenging scenarios to facilitate assessing the...
Preprint
Full-text available
Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model requires numerous labeled training samples, and the number and quality of these samples directly affect the representational capability of the trained model. However, this approach is restricti...
Article
In visual tracking, it is challenging to distinguish the target from similar objects called noises in the background. As deep trackers use convolutional neural networks for image classification as feature extractors, the extracted features are insensitive to different instances in the same class, which is prone to make prediction models confuse the...
Article
Full-text available
Existing trackers usually exploit robust features or online updating mechanisms to deal with target variations which is a key challenge in visual tracking. However, the features being robust to variations remain little spatial information, and existing online updating methods are prone to overfitting. In this paper, we propose a dual-margin model f...
Article
Full-text available
Recent years have witnessed significant improvements of ensemble trackers based on independent models. However, existing ensemble trackers only combine the responses of independent models and pay less attention to the learning process, which hinders their performance from further improvements. To this end, we propose an interactive learning framewo...
Article
Full-text available
The training of a feature extraction network typically requires abundant manually annotated training samples, making this a time-consuming and costly process. Accordingly, we propose an effective self-supervised learning-based tracker in a deep correlation framework (named: self-SDCT). Motivated by the forward-backward tracking consistency of a rob...
Article
Full-text available
Existing regression based tracking methods built on correlation filter model or convolution modeldo not take both accuracy and robustness into account at the same time. In this paper, we pro-pose a dual regression framework comprising a discriminative fully convolutional module and a fine-grained correlation filter component for visual tracking. Th...
Preprint
Full-text available
In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an evaluation dataset and a training dataset with a total of 1,400 TIR sequences and more than 600K frames. We annotate the bounding box of objects in every frame of all sequences and generate over...
Conference Paper
Full-text available
In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTB-TIR, which consists of an evaluation dataset and a training dataset with a total of 1,400 TIR sequences and more than 600K frames. We annotate the bounding box of objects in every frame of all sequences and generate over...
Article
Full-text available
While part-based methods have been shown effective in the person re-identification task, it is unreasonable for most of them to treat each part equally, due to the retrieved image may be affected by deformation, occlusion and other factors, which makes the feature information of some parts unreliable. Instead of using the same weight of each part f...
Article
Full-text available
Existing deep Thermal InfraRed (TIR) trackers only use semantic features to represent the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB images. To address this issue, we propose a multi-level similarity model under a Siamese frame...
Conference Paper
Full-text available
Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific di...
Article
Convolutional Neural Networks (CNN) have been demonstrated to achieve state-of-the-art performance in visual object tracking task. However, existing CNN-based trackers usually use holistic target samples to train their networks. Once the target undergoes complicated situations (e.g., occlusion, background clutter, and deformation), the tracking per...
Data
Multi-Task Driven Feature Models for Thermal Infrared Tracking--Supplementary Materials
Conference Paper
Full-text available
Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However , these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific d...
Preprint
Full-text available
Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific di...
Article
Full-text available
Thermal infrared (TIR) pedestrian tracking is one of the important components among the numerous applications of computer vision, which has a major advantage: it can track pedestrians in total darkness. The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field. However, there...
Preprint
Full-text available
Existing deep Thermal InfraRed (TIR) trackers only use semantic features to describe the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB images.To address this issue, we propose a multi-level similarity model under a Siamese framewo...
Article
The performance of the tracking task directly depends on target object appearance features. Therefore, a robust method for constructing appearance features is crucial for avoiding tracking failure. The tracking methods based on Convolution Neural Network (CNN) have exhibited excellent performance in the past years. However, the features from each o...
Article
Full-text available
Discriminative methods have been widely applied to construct the appearance model for visual tracking. Most existing methods incorporate online updating strategy to adapt to the appearance variations of targets. The focus of online updating for discriminative methods is to select the positive samples emerged in past frames to represent the appearan...
Article
Full-text available
In the most tracking approaches, a score function is utilized to determine which candidate is the optimal one by measuring the similarity between the candidate and the template. However, the representative samples selection in the template update is challenging. To address this problem, in this paper, we treat the template as a linear combination o...
Conference Paper
Full-text available
In this paper, we propose a novel thermal infrared (TIR) tracker via a deep Siamese convolutional neural network (CNN), named Siamese tir. Different from the most existing discriminative TIR tracking methods which treat the tracking problem as a classification problem, we treat the TIR tracking problem as a similarity verification problem. Specific...
Article
Most thermal infrared (TIR) tracking methods are discriminative, which treat the tracking problem as a classification task. However, the objective of the classifier (label prediction) is not coupled to the objective of the tracker (location estimation). The classification task focuses on the between-class difference of the arbitrary objects, while...
Article
Full-text available
In the past years, discriminative methods are popular in visual tracking. The main idea of the discriminative method is to learn a classifier to distinguish the target from the background. The key step is the update of the classifier. Usually, the tracked results are chosen as the positive samples to update the classifier, which results in the fail...
Article
Full-text available
Unlike the visual object tracking, thermal infrared object tracking can track a target object in total darkness. Therefore, it has broad applications, such as in rescue and video surveillance at night. However, there are few studies in this field mainly because thermal infrared images have several unwanted attributes, which make it difficult to obt...
Article
Full-text available
Robustness and efficiency are the two main goals of existing trackers. Most robust trackers are implemented with combined features or models accompanied with a high computational cost. To achieve a robust and efficient tracking performance, we propose a multi-view correlation tracker to do tracking. On one hand, the robustness of the tracker is enh...
Article
Full-text available
Visual tracking remains a challenging problem in computer vision due to the intricate variation of target appearances. Some progress made in recent years has revealed that correlation filters, which formulate the tracking process by creating a regressor in the frequency domain, have achieved remarkable experimental results on a large amount of vide...

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Projects

Projects (2)
Project
Our goal is to promote the development of thermal infrared object tracking.