scholarly journals Visual Tracking Using Max-Average Pooling and Weight-Selection Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Suguo Zhu ◽  
Junping Du
Keyword(s):  
Particle Filter ◽  
Sparse Representation ◽  
Visual Tracking ◽  
Spatial Information ◽  
State Of The Art ◽  
Experimental Results ◽  
Selection Strategy ◽  
Illumination Change ◽  
Spatial Pooling ◽  
Hybrid Framework

Many modern visual tracking algorithms incorporate spatial pooling, max pooling, or average pooling, which is to achieve invariance to feature transformations and better robustness to occlusion, illumination change, and position variation. In this paper, max-average pooling method and Weight-selection strategy are proposed with a hybrid framework, which is combined with sparse representation and particle filter, to exploit the spatial information of an object and make good compromises to ensure the correctness of the results in this framework. Challenges can be well considered by the proposed algorithm. Experimental results demonstrate the effectiveness and robustness of the proposed algorithm compared with the state-of-the-art methods on challenging sequences.

Efficient image fusion with approximate sparse representation

2016 ◽  
Vol 14 (04) ◽  
pp. 1650024 ◽  
Author(s):  
Yang Bin ◽  
Yang Chao ◽  
Huang Guoyu
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
State Of The Art ◽  
Experimental Results ◽  
Computational Time ◽  
Selection Strategy ◽  
Image Patches

In this paper, an efficient approximate sparse representation (SR) algorithm with multi-selection strategy is used to solve the image fusion problem. We have shown that the approximate SR is effective for image fusion even if the sparse coefficients are not the sparsest ones possible. A multi-selection strategy is used to accelerate the process of generating the approximate sparse coefficients which are used to guide the fusion of image patches. The relative parameters are also investigated experimentally to further reduce the computational time. The proposed method is compared with some state-of-the-art image fusion approaches on several pairs of multi-source images. The experimental results exhibit that the proposed method is able to yield superior fusion results with less consumption time.

scholarly journals Part-Based Visual Tracking via Online Weighted P-N Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Heng Fan ◽  
Jinhai Xiang ◽  
Jun Xu ◽  
Honghong Liao
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
The State ◽  
Tracking Algorithm ◽  
Local Feature ◽  
Experimental Results ◽  
Learning Method ◽  
Target Model ◽  
Tracking Process

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.

Robust Inland Waterway Ship Tracking via Hybrid TLD and Kalman Filter

2014 ◽  
Vol 1037 ◽  
pp. 373-377 ◽  
Author(s):  
Teng Fei ◽  
Liu Qing ◽  
Lin Zhu ◽  
Jing Li
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
State Of The Art ◽  
Experimental Results ◽  
Closed Circuit ◽  
Video Sequences ◽  
Inland Waterway ◽  
Cluttered Background ◽  
Full Occlusion

In this paper, we mainly address the problem of tracking a single ship in inland waterway CCTV (Closed-Circuit Television) video sequences. Although state-of-the-art performance has been demonstrated in TLD (Tracking-Learning-Detection) visual tracking, it is still challenging to perform long-term robust ship tracking due to factors such as cluttered background, scale change, partial or full occlusion and so forth. In this work, we focus on tracking a single ship when it suffers occlusion. To accomplish this goal, an effective Kalman filter is adopted to construct a novel online model to adapt to the rapid ship appearance change caused by occlusion. Experimental results on numerous inland waterway CCTV video sequences demonstrate that the proposed algorithm outperforms the original one.

scholarly journals Loss-based active learning via double-branch deep network

2021 ◽  
Vol 18 (5) ◽  
pp. 172988142110449
Author(s):  
Qiang Fang ◽  
Xin Xu ◽  
Dengqing Tang
Keyword(s):  
Active Learning ◽  
Visual Recognition ◽  
State Of The Art ◽  
Experimental Results ◽  
Research Interest ◽  
Selection Strategy ◽  
Network Architectures ◽  
Data Annotation ◽  
Deep Network ◽  
Effectiveness And Efficiency

Due to the limitation of data annotation and the ability of dealing with label-efficient problems, active learning has received lots of research interest in recent years. Most of the existing approaches focus on designing a different selection strategy to achieve better performance for special tasks; however, the performance of the strategy still needs to be improved. In this work, we focus on improving the performance of active learning and propose a loss-based strategy that learns to predict target losses of unlabeled inputs to select the most uncertain samples, which is designed to learn a better selection strategy based on a double-branch deep network. Experimental results on two visual recognition tasks show that our approach achieves the state-of-the-art performance compared with previous methods. Moreover, our approach is also robust to different network architectures, biased initial labels, noisy oracles, or sampling budget sizes, and the complexity is also competitive, which demonstrates the effectiveness and efficiency of our proposed approach.

Visual Tracking with Multilevel Sparse Representation and Metric Learning

2018 ◽  
Vol 11 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Baifan Chen ◽  
Meng Peng ◽  
Lijue Liu ◽  
Tao Lu
Keyword(s):  
Sparse Representation ◽  
Visual Tracking ◽  
Hierarchical Structure ◽  
Real World ◽  
State Of The Art ◽  
Metric Learning ◽  
The State ◽  
Discriminative Ability ◽  
The Real ◽  
The Hierarchical Structure

Visual tracking arises in various real-world tasks where an object should be located in a video. Sparse representation can implement tracking problems by linearly representing object with a few templates. However, this approach has two main shortcomings. Namely, setting the templates updating frequency is difficult and meanwhile it is relatively weak in distinguishing the object from the background. For solving these problems, the author models a multilevel object template set that can be stratified by different updating time spans. The hierarchical structure and updating strategy promise the real-timeness, stability, and diversity of object template. Additionally, metric learning is combined to evaluate the object candidates and thereby improve the discriminative ability. Experiments on well-known visual tracking datasets demonstrate that the proposed method can track an object more robustly and accurately compared to the state-of-the-art approaches.

scholarly journals Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing

2021 ◽  
Vol 13 (16) ◽  
pp. 3147
Author(s):  
Ziqiang Hua ◽  
Xiaorun Li ◽  
Jianfeng Jiang ◽  
Liaoying Zhao
Keyword(s):  
Real World ◽  
Spatial Information ◽  
State Of The Art ◽  
The State ◽  
Spectral Unmixing ◽  
Experimental Results ◽  
Spectral Information ◽  
Hyperspectral Unmixing ◽  
Spatial Features ◽  
Gating Mechanism

Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it difficult to balance their effects on unmixing results. In this paper, we propose two gated autoencoder networks with the intention of adaptively controlling the contribution of spectral and spatial features in unmixing process. Gating mechanism is adopted in the networks to filter and regularize spatial features to construct an unmixing algorithm based on spectral information and supplemented by spatial information. In addition, abundance sparsity regularization and gating regularization are introduced to ensure the appropriate implementation. Experimental results validate the superiority of the proposed method to the state-of-the-art techniques in both synthetic and real-world scenes. This study confirms the effectiveness of gating mechanism in improving the accuracy and efficiency of utilizing spatial signatures for spectral unmixing.

scholarly journals Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Li Jia Wang ◽  
Hua Zhang
Keyword(s):  
Visual Tracking ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Tracking Algorithm ◽  
Inner Product ◽  
Selection Strategy ◽  
Weak Classifier ◽  
Update Strategy ◽  
Appearance Changes

An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probabilityMtimes. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes.

scholarly journals Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting

2021 ◽  
Vol 11 (3) ◽  
pp. 953
Author(s):  
Jin Hong ◽  
Junseok Kwon
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Visual Tracking ◽  
State Of The Art ◽  
Experimental Results ◽  
Tracking Method ◽  
Aerial Vehicles ◽  
Object Proposal ◽  
Background Clutter ◽  
Art Performance ◽  
Over Time

In this paper, we propose a novel visual tracking method for unmanned aerial vehicles (UAVs) in aerial scenery. To track the UAVs robustly, we present a new object proposal method that can accurately determine the object regions that are likely to exist. The proposed object proposal method is robust to small objects and severe background clutter. For this, we vote on candidate areas of the object and increase or decrease the weight of the area accordingly. Thus, the method can accurately propose the object areas that can be used to track small-sized UAVs with the assumption that their motion is smooth over time. Experimental results verify that UAVs are accurately tracked even when they are very small and the background is complex. The proposed method qualitatively and quantitatively delivers state-of-the-art performance in comparison with conventional object proposal-based methods.

Enhanced MIL Visual Tracking via Distribution Fields Descriptor

2014 ◽  
Vol 571-572 ◽  
pp. 725-728
Author(s):  
Kang Sun ◽  
Nian Nian Sun
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Experimental Results ◽  
Video Sequences ◽  
Object Model ◽  
Object Appearance ◽  
Distribution Field

This paper presents an enhanced multiple instance learning (MIL) tracker with a suitable representation of object appearance called distribution field descriptor (DF). To address transformations of object template (rotation, scaling), we firstly replace the smoothed histograms used in DF with smoothed bins according the theory of averaged shifted histograms. Secondly, due to the DF specificity and landscape smoothness, we adopt DF descriptor instead of traditional Haar-like one to represent the object appearance. By build object model using selected discriminative layers, our tracker is more robust while needing fewer features than the original tracker. The experimental results show higher performances of our tracker than those of five state-of-the-art ones on several challenging video sequences.

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