Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring

2021 ◽  
pp. 1-1
Author(s):  
Shuai Liu ◽  
Shuai Wang ◽  
Xinyu Liu ◽  
Amir H. Gandomi ◽  
Mahmoud Daneshmand ◽  
...  
Keyword(s):  
Human Memory ◽  
Visual Monitoring ◽  
Template Update ◽  
Update Strategy

scholarly journals A Novel Incremental Multi-Template Update Strategy for Robust Object Tracking

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 162668-162682
Author(s):  
Qingsong Xie ◽  
Kewei Liu ◽  
An Zhiyong ◽  
Lei Wang ◽  
Ye Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Template Update ◽  
Update Strategy

scholarly journals Discriminative and Robust Online Learning for Siamese Visual Tracking

2020 ◽  
Vol 34 (07) ◽  
pp. 13017-13024 ◽  
Author(s):  
Jinghao Zhou ◽  
Peng Wang ◽  
Haoyang Sun
Keyword(s):  
Discriminative Learning ◽  
Visual Object ◽  
Specific Context ◽  
Visual Object Tracking ◽  
Robust Learning ◽  
Online Module ◽  
Recent Success ◽  
Template Update ◽  
Update Strategy ◽  
Siamese Networks

The problem of visual object tracking has traditionally been handled by variant tracking paradigms, either learning a model of the object's appearance exclusively online or matching the object with the target in an offline-trained embedding space. Despite the recent success, each method agonizes over its intrinsic constraint. The online-only approaches suffer from a lack of generalization of the model they learn thus are inferior in target regression, while the offline-only approaches (e.g., convolutional siamese trackers) lack the target-specific context information thus are not discriminative enough to handle distractors, and robust enough to deformation. Therefore, we propose an online module with an attention mechanism for offline siamese networks to extract target-specific features under L2 error. We further propose a filter update strategy adaptive to treacherous background noises for discriminative learning, and a template update strategy to handle large target deformations for robust learning. Effectiveness can be validated in the consistent improvement over three siamese baselines: SiamFC, SiamRPN++, and SiamMask. Beyond that, our model based on SiamRPN++ obtains the best results over six popular tracking benchmarks and can operate beyond real-time.

scholarly journals Robust Online Object Tracking Based on Feature Grouping and 2DPCA

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Ming-Xin Jiang ◽  
Jun-Xing Zhang ◽  
Min Li
Keyword(s):  
Object Tracking ◽  
Principal Component ◽  
Subspace Learning ◽  
Tracking Algorithm ◽  
Posteriori Probability ◽  
Error Matrix ◽  
A Posteriori Probability ◽  
Template Update ◽  
Feature Grouping ◽  
Update Strategy

We present an online object tracking algorithm based on feature grouping and two-dimensional principal component analysis (2DPCA). Firstly, we introduce regularization into the 2DPCA reconstruction and develop an iterative algorithm to represent an object by 2DPCA bases. Secondly, the object templates are grouped into a more discriminative image and a less discriminative image by computing the variance of the pixels in multiple frames. Then, the projection matrix is learned according to the more discriminative image and the less discriminative image, and the samples are projected. The object tracking results are obtained using Bayesian maximum a posteriori probability estimation. Finally, we employ a template update strategy which combines incremental subspace learning and the error matrix to reduce tracking drift. Compared with other popular methods, our method reduces the computational complexity and is very robust to abnormal changes. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm achieves more favorable performance than several state-of-the-art methods.

Mean-Shift Guided Particle Filter Tracking Method Based on Layered Dynamic Template Update

2013 ◽  
Vol 846-847 ◽  
pp. 1217-1220
Author(s):  
Yuan Zheng Li
Keyword(s):  
Particle Filter ◽  
Mean Shift ◽  
Tracking Method ◽  
Mean Shift Algorithm ◽  
Template Update ◽  
Complex Scenes ◽  
One Step ◽  
The One ◽  
Dynamic Template ◽  
Update Strategy

Traditional tracking algorithm is not compatible between robustness and efficiency, under complex scenes, the stable template update strategy is not robust to target appearance changes. Therefore, the paper presents a dynamic template-update method that combined with a mean-shift guided particle filter tracking method. By incorporating the original information into the updated template, or according to the variety of each component in template to adjust the updating weights adaptively, the presented algorithm has the natural ability of anti-drift. Besides, the proposed method cope the one-step iteration of mean-shift algorithm with the particle filter, thus boost the performance of efficiency. Experimental results show the feasibility of the proposed algorithm in this paper.

scholarly journals Visual Object Tracking Based on 2DPCA and ML

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Ming-Xin Jiang ◽  
Min Li ◽  
Hong-Yu Wang
Keyword(s):  
Object Tracking ◽  
Principal Component ◽  
Likelihood Estimation ◽  
Subspace Learning ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Error Matrix ◽  
Template Update ◽  
Update Strategy

We present a novel visual object tracking algorithm based on two-dimensional principal component analysis (2DPCA) and maximum likelihood estimation (MLE). Firstly, we introduce regularization into the 2DPCA reconstruction and develop an iterative algorithm to represent an object by 2DPCA bases. Secondly, the model of sparsity constrained MLE is established. Abnormal pixels in the samples will be assigned with low weights to reduce their effects on the tracking algorithm. The object tracking results are obtained by using Bayesian maximum a posteriori (MAP) probability estimation. Finally, to further reduce tracking drift, we employ a template update strategy which combines incremental subspace learning and the error matrix. This strategy adapts the template to the appearance change of the target and reduces the influence of the occluded target template as well. Compared with other popular methods, our method reduces the computational complexity and is very robust to abnormal changes. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm achieves more favorable performance than several state-of-the-art methods.

Precision feature point tracking method using a drift-correcting template update strategy

2009 ◽  
Author(s):  
Xiaoming Peng ◽  
Qian Ma ◽  
Qiheng Zhang ◽  
Wufan Chen ◽  
Zhiyong Xu
Keyword(s):  
Feature Point ◽  
Tracking Method ◽  
Point Tracking ◽  
Feature Point Tracking ◽  
Template Update ◽  
Update Strategy

scholarly journals Deep Metric Learning-Assisted 3D Audio-Visual Speaker Tracking via Two-Layer Particle Filter

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yidi Li ◽  
Hong Liu ◽  
Bing Yang ◽  
Runwei Ding ◽  
Yang Chen
Keyword(s):  
Particle Filter ◽  
Metric Learning ◽  
Image Plane ◽  
Observation Model ◽  
3D Audio ◽  
Speaker Tracking ◽  
Template Update ◽  
The Hierarchical Structure ◽  
Deep Metric Learning ◽  
Update Strategy

For speaker tracking, integrating multimodal information from audio and video provides an effective and promising solution. The current challenges are focused on the construction of a stable observation model. To this end, we propose a 3D audio-visual speaker tracker assisted by deep metric learning on the two-layer particle filter framework. Firstly, the audio-guided motion model is applied to generate candidate samples in the hierarchical structure consisting of an audio layer and a visual layer. Then, a stable observation model is proposed with a designed Siamese network, which provides the similarity-based likelihood to calculate particle weights. The speaker position is estimated using an optimal particle set, which integrates the decisions from audio particles and visual particles. Finally, the long short-term mechanism-based template update strategy is adopted to prevent drift during tracking. Experimental results demonstrate that the proposed method outperforms the single-modal trackers and comparison methods. Efficient and robust tracking is achieved both in 3D space and on image plane.

scholarly journals Robust Correlation Tracking for UAV Videos via Feature Fusion and Saliency Proposals

2018 ◽  
Vol 10 (10) ◽  
pp. 1644 ◽  
Author(s):  
Xizhe Xue ◽  
Ying Li ◽  
Hao Dong ◽  
Qiang Shen
Keyword(s):  
Single Channel ◽  
Feature Fusion ◽  
Low Cost ◽  
Correlation Filter ◽  
Correlation Filters ◽  
Illumination Variation ◽  
Channel Response ◽  
Template Update ◽  
Color Name ◽  
Update Strategy

Following the growing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on object tracking using videos recorded from UAVs. However, tracking from UAV videos poses many challenges due to platform motion, including background clutter, occlusion, and illumination variation. This paper tackles these challenges by proposing a correlation filter-based tracker with feature fusion and saliency proposals. First, we integrate multiple feature types such as dimensionality-reduced color name (CN) and histograms of oriented gradient (HOG) features to improve the performance of correlation filters for UAV videos. Yet, a fused feature acting as a multivector descriptor cannot be directly used in prior correlation filters. Therefore, a fused feature correlation filter is proposed that can directly convolve with a multivector descriptor, in order to obtain a single-channel response that indicates the location of an object. Furthermore, we introduce saliency proposals as re-detector to reduce background interference caused by occlusion or any distracter. Finally, an adaptive template-update strategy according to saliency information is utilized to alleviate possible model drifts. Systematic comparative evaluations performed on two popular UAV datasets show the effectiveness of the proposed approach.

Sign in / Sign up

Export Citation Format

Share Document