Adaptive Compressive Tracking Algorithm Based on SIFT Features

2015 ◽  
Author(s):  
Haiyan Yang ◽  
Xinhua Jiang ◽  
Sheng Gao
Keyword(s):  
Tracking Algorithm ◽  
Compressive Tracking ◽  
Sift Features

scholarly journals An Improved Fast Compressive Tracking Algorithm Based on Online Random Forest Classifier

2016 ◽  
Vol 59 ◽  
pp. 01003 ◽  
Author(s):  
Jintao Xiong ◽  
Pan Jiang ◽  
Jianyu Yang ◽  
Zhibin Zhong ◽  
Ran Zou ◽  
...  
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Tracking Algorithm ◽  
Compressive Tracking

A Tracking Window Adaptive Compressive Tracking Algorithm

2019 ◽  
Vol 33 (14) ◽  
pp. 1959045 ◽  
Author(s):  
Wenhao Wang ◽  
Mingxin Jiang ◽  
Xiaobing Chen ◽  
Li Hua ◽  
Shangbing Gao
Keyword(s):  
Texture Features ◽  
Candidate Region ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Scale Invariant ◽  
Compressive Tracking ◽  
Multi Scale ◽  
Feature Normalization ◽  
Object Region ◽  
Candidate Regions

In the original compression tracking algorithm, the size of the tracking box is fixed. There should be better tracking results for scale-invariant objects, but worse tracking results for scale-variant objects. To overcome this defect, a scale-adaptive compressive tracking (CT) algorithm is proposed. First of all, the imbalance of the gray and texture features in the original CT algorithm is balanced by the multi-feature method, which makes the algorithm more robust. Then, searching different candidate regions by using the method of multi-scale search along with feature normalization makes the features extracted from different scales comparable. Finally, the candidate region with the maximum discriminate degree is selected as the object region. Thus, the tracking-box size is adaptive. The experimental results show that when the object scale changes, the improving CT algorithm has higher accuracy and robustness than the original CT algorithm.

scholarly journals Target Tracking Algorithm Using Angular Point Matching Combined with Compressive Tracking

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Jing Luo ◽  
Tingting Dong ◽  
Chunyuan Zi ◽  
Chunbo Xiu ◽  
Huixin Tian ◽  
...  
Keyword(s):  
Target Position ◽  
Tracking Algorithm ◽  
Angular Point ◽  
Measurement Matrix ◽  
Illumination Change ◽  
Tracking Errors ◽  
Point Matching ◽  
Compressive Tracking ◽  
Pose Variation ◽  
Better Than

To solve the problems of tracking errors such as target missing that emerged in compressive tracking (CT) algorithm due to factors such as pose variation, illumination change, and occlusion, a novel tracking algorithm combined angular point matching with compressive tracking (APMCCT) was proposed. A sparse measurement matrix was adopted to extract the Haar-like features. The offset of the predicted target position was integrated into the angular point matching, and the new target position was calculated. Furthermore, the updating mechanism of the template was optimized. Experiments on different video sequences have shown that the proposed APMCCT performs better than CT algorithm in terms of accuracy and robustness and adaptability to pose variation, illumination change, and occlusion.

Variable Scale Compressive Tracking Based on Structural Constraint Sample

2014 ◽  
Vol 488-489 ◽  
pp. 1074-1078
Author(s):  
Lu Ping Zhang ◽  
Meng Cai ◽  
Biao Li ◽  
Lu Ping Wang
Keyword(s):  
State Of The Art ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Structural Constraint ◽  
Scale Problem ◽  
Scale Selection ◽  
Compressive Tracking ◽  
Image Patches ◽  
Basic Attitude ◽  
Selection Of

A variable scale compressive tracking algorithm based on structural constraint sample is presented to solve the variable scale problem in this paper. A number of scanning windows with different scales and positions are obtained by structural constraint sampling.Some sparse random sensing matrices with different scales that can be computed offline easily are adopted to extract the features of different foreground target and background sample image patches with relevant scales online, the sample patch having a maximal score is regarded as the new tracking result by classifying the compressive features via a naive bayesian classifier,meanwhile,to update the location and scale. Experimental results show the proposed algorithm performs favorably against state-of-the-art algorithms on challenging sequences in terms of the basic attitude and scale change, which is robust and does not depend on the scale selection of the initial tracking area.

Particle Filter Target Tracking Algorithm Based on the SIFT and Color Features Fusion

2015 ◽  
Vol 734 ◽  
pp. 476-481
Author(s):  
Ming Hua Liu ◽  
Chuan Sheng Wang ◽  
Xian Lun Wang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Dynamic Change ◽  
Tracking Algorithm ◽  
Complex Environments ◽  
Complex Scene ◽  
Features Fusion ◽  
Single Feature ◽  
Tracking Process ◽  
Sift Features

Aiming at the poor robustness problem of using single feature in the target tracking process, a novel tracking algorithm based on color and SIFT features fusion in particle filter framework is presented in complex environments. Color and SIFT features are selected to establish the target model according to their stability, The scale and rotation invariance of SIFT feature and resistance occlusion property of color feature has been fused in the particle filter framework adaptively. According to the dynamic change of the tracking scene, the fusion weights is updated adaptively. Experimental results show the proposed method can track target robustly under complex scene in real-time performance.

Sign in / Sign up

Export Citation Format

Share Document