An adaptive window object tracking algorithm based on variable resolution

2011 ◽  
Vol 19 (2) ◽  
Author(s):  
L. Li
Keyword(s):  
Object Tracking ◽  
Visual Tracking ◽  
Posterior Probability ◽  
Sampling Method ◽  
Tracking Algorithm ◽  
Variable Resolution ◽  
Tracking Method ◽  
Adaptive Window ◽  
Maximum Posterior Probability ◽  
Probability Index

AbstractThis paper presents an adaptive window object tracking method based on variable resolution. It copes with the change in size of the object during visual tracking. On the basis of the visual tracking algorithm, based on maximum posterior probability, we analyze the posterior probability index on the inside and outside panes of the object window, then build a mathematical model for adjusting object size with an adaptive window. Since the resolution changes according to the size of the object, this thesis uses a statistical sampling method of the feature by variable resolution. The resolution of the statistical feature is correspondingly changed in object tracking with an adaptive window. The resolution of a larger object is decreased, which realizes an object tracking method with adaptive window based on variable resolution.

ROBUST AND FAST TRACKING ALGORITHM IN VIDEO SEQUENCES BY ADAPTIVE WINDOW SIZING USING A NOVEL ANALYSIS ON SPATIOTEMPORAL GRADIENT POWERS

2007 ◽  
Vol 16 (02) ◽  
pp. 305-317 ◽  
Author(s):  
PAYMAN MOALLEM ◽  
ALIREZA MEMARMOGHADDAM ◽  
MOHSEN ASHOURIAN
Keyword(s):  
Window Size ◽  
Image Sequences ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Occupancy Rate ◽  
Tracking Method ◽  
Fast Tracking ◽  
Adaptive Window ◽  
Real Video ◽  
Adaptively Adjusting

Success of a tracking method depends largely on choosing the suitable window size as soon as the target size changes in image sequences. To achieve this goal, we propose a fast tracking algorithm based on adaptively adjusting tracking window. Firstly, tracking window is divided into four edge subwindows, and a background subwindow around it. Then, by calculating the spatiotemporal gradient power ratios of the target in each subwindow, four proper expansion vectors are associated with any tracking window sides such that the occupancy rate of the target in tracking window should be maintained within a specified range. In addition, since temporal changing of target is evaluated in calculating these vectors, we estimate overall target displacement by sum of expansion vectors. Experimental results using various real video sequences show that the proposed algorithm successfully track an unknown textured target in real time, and is robust to dynamic occlusions in complex noisy backgrounds.

Multi-Object Tracking with Single Camera

2015 ◽  
Vol 740 ◽  
pp. 668-671
Author(s):  
Yu Bing Dong ◽  
Ying Sun ◽  
Ming Jing Li
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Block Matching ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Single Camera ◽  
Trajectory Prediction ◽  
Tracking Method

Multi-object tracking has been a challenging topic in computer vision. A Simple and efficient moving multi-object tracking algorithm is proposed. A new tracking method combined with trajectory prediction and a sub-block matching is used to handle the objects occlusion. The experimental results show that the proposed algorithm has good performance.

scholarly journals Robust Visual Tracking Using the Bidirectional Scale Estimation

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
An Zhiyong ◽  
Guan Hao ◽  
Li Jinjiang
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Visual Tracking ◽  
Ridge Regression ◽  
State Of The Art ◽  
Benchmark Dataset ◽  
Tracking Algorithm ◽  
Conservative Strategy ◽  
Scale Estimation ◽  
Scale Variation

Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy.

scholarly journals Long-term target tracking combined with re-detection

2020 ◽  
Author(s):  
ZengShun Zhao ◽  
Juanjuan Wang ◽  
HaoRan Yang ◽  
Ning Xu ◽  
Chengqin Wu ◽  
...  
Keyword(s):  
Visual Tracking ◽  
Correlation Energy ◽  
State Of The Art ◽  
Tracking Algorithm ◽  
Correlation Filters ◽  
Short Term ◽  
Tracking Method ◽  
Svm Model ◽  
Confidence Degree

Abstract The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, most existing methods have not been done and their performances have also been limited. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Discriminative Correlation Filters (DCF) tracking algorithm with a re-detection component based on the SVM model. The DCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 dataset, the experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

Multi-object Tracking Method Based on Efficient Channel Attention and Switchable Atrous Convolution

2021 ◽  
Author(s):  
Xuezhi Xiang ◽  
Wenkai Ren ◽  
Yujian Qiu ◽  
Kaixu Zhang ◽  
Ning Lv
Keyword(s):  
Object Tracking ◽  
Tracking Method

Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features

2020 ◽  
pp. 2155002
Author(s):  
Xiuhua Hu ◽  
Yuan Chen ◽  
Yan Hui ◽  
Yingyu Liang ◽  
Guiping Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Scale Model ◽  
Correlation Filters ◽  
Motion Feature ◽  
Tracking Method ◽  
Object State ◽  
Multi Scale ◽  
Tracking Process ◽  
Object Appearance ◽  
Response Peak

Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

High speed long-term visual object tracking algorithm for real robot systems

2021 ◽  
Vol 434 ◽  
pp. 268-284
Author(s):  
Muxi Jiang ◽  
Rui Li ◽  
Qisheng Liu ◽  
Yingjing Shi ◽  
Esteban Tlelo-Cuautle
Keyword(s):  
Object Tracking ◽  
High Speed ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Robot Systems ◽  
Real Robot
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