scholarly journals Multifeatures Based Compressive Sensing Tracking

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Liang He ◽  
Yuming Bo ◽  
Gaopeng Zhao
Keyword(s):  
Compressed Sensing ◽  
Compressive Sensing ◽  
Particle Filter ◽  
Approximation Problem ◽  
Sparse Approximation ◽  
Experimental Results ◽  
Multiple Features ◽  
Target Template ◽  
Nonnegative Constraints

To benefit from the development of compressive sensing, we cast tracking as a sparse approximation problem in a particle filter framework based on multifeatures. In this framework, the target template is composed of multiple features extracted from visible and infrared frames; in addition, occlusion, interruption, and noises are addressed through a set of trivial templates. With this model, the sparsity is achieved via a compressive sensing approach without nonnegative constraints; then the residual between sparsity representation and the compressed sensing observation is used to measure the likelihood which weights particles. After that, the target template is adaptively updated according to the Bhattacharyya coefficients. Some experimental results demonstrate that the proposed tracker appears to have better robustness compared with four different algorithms.

Study on Over-Complete Dictionaries for Sparse Representations of Signals

2012 ◽  
Vol 157-158 ◽  
pp. 796-799
Author(s):  
Guang Chun Gao ◽  
Kai Xiong ◽  
Li Na Shang ◽  
Sheng Ying Zhao ◽  
Cui Zhang
Keyword(s):  
Inverse Problems ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Sparse Representations ◽  
Experimental Results ◽  
Decomposition Algorithm ◽  
Linear Combinations ◽  
Design Algorithm ◽  
Sparse Decomposition

In recent years there has been a growing interest in the study of sparse representation of signals. The redundancy of over-complete dictionary can make it effectively capture the characteristics of the signals. Using an over-complete dictionary that contains prototype signal-atoms, signals are described as linear combinations of a few of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, Compressed Sensing (CS), and more. Recent activities in this field concentrate mainly on the study of sparse decomposition algorithm and dictionary design algorithm. In this paper, we discuss the advantages of sparse dictionaries, and present the implicit dictionaries for signal sparse presents. The overcomplete dictionaries which combined the different orthonormal transform bases can be used for the compressed sensing. Experimental results demonstrate the effectivity for sparse presents of signals.

Review of Sparsity Known and Blind Sparsity Greedy Algorithms for Compressed Sensing

2013 ◽  
pp. 125-138
Author(s):  
Chun-Yan Zeng ◽  
Li-Hong Ma ◽  
Ming-Hui Du ◽  
Jing Tian
Keyword(s):  
Compressed Sensing ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Greedy Algorithms ◽  
Diagrammatic Representation ◽  
Support Set ◽  
Exact Reconstruction ◽  
Signal Characteristics ◽  
Sparsity Level

Sparsity level is crucial to Compressive Sensing (CS) reconstruction, but in practice it is often unknown. Recently, several blind sparsity greedy algorithms have emerged to recover signals by exploiting the underlying signal characteristics. Sparsity Adaptive Matching Pursuit (SAMP) estimates the sparsity level and the true support set stage by stage, while Backtracking-Based Adaptive OMP (BAOMP) selects atoms by thresholds related to the maximal residual projection. This chapter reviews typical sparsity known greedy algorithms including OMP, StOMP, and CoSaMP, as well as those emerging blind sparsity greedy algorithms. Furthermore, the algorithms are analysed in structured diagrammatic representation and compared by exact reconstruction probabilities for Gaussian and binary signals distributed sparsely.

scholarly journals Particle Filter Vehicles Tracking by Fusing Multiple Features

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 133694-133706 ◽  
Author(s):  
Yu Wang ◽  
Xiaojuan Ban ◽  
Huan Wang ◽  
Xiaorui Li ◽  
Zixuan Wang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Multiple Features

scholarly journals Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 436
Author(s):  
Yunfei Cheng ◽  
Ying Hu ◽  
Mengshu Hou ◽  
Tongjie Pan ◽  
Wenwen He ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Compressed Sensing ◽  
Health Monitoring ◽  
Prior Information ◽  
Classification Performance ◽  
Experimental Results ◽  
Measurement Matrix ◽  
Ecg Signals ◽  
Proposed Model ◽  
Atrial Fibrillation Detection

In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did not fully benefit from the existing prior information, resulting in unsatisfactory classification performance, especially in some applications that require high compression ratio (CR). In this paper, we propose a deep learning method to detect atrial fibrillation directly from compressed ECG without reconstruction. Specifically, we design a deep network model for one-dimensional ECG signals, and the measurement matrix is used to initialize the first layer of the model so that the proposed model can obtain more prior information which benefits improving the classification performance of atrial fibrillation detection from compressed ECG. The experimental results on the MIT-BIH Atrial Fibrillation Database show that when the CR is 10%, the accuracy and F1 score of the proposed method reach 97.52% and 98.02%, respectively. Compared with the atrial fibrillation detection from original ECG, the corresponding accuracy and F1 score are only reduced by 0.88% and 0.69%. Even at a high CR of 90%, the accuracy and F1 score are still only reduced by 6.77% and 5.31%, respectively. All of the experimental results demonstrate that the proposed method is superior to other existing methods for atrial fibrillation detection from compressed ECG. Therefore, the proposed method is promising for atrial fibrillation detection in wearable health monitoring based on compressed sensing.

Pedestrian Tracking with HOG and Color Histogram Features

2012 ◽  
Vol 241-244 ◽  
pp. 498-501
Author(s):  
Lie Guo ◽  
Guang Xi Zhang ◽  
Ping Shu Ge ◽  
Lin Hui Li
Keyword(s):  
Particle Filter ◽  
Target Position ◽  
Color Histogram ◽  
Experimental Results ◽  
Pedestrian Tracking ◽  
Particle Sampling ◽  
Histograms Of Oriented Gradients ◽  
Initial Target

To improve the effectiveness of pedestrian tracking, the histograms of oriented gradients (HOG) and color histogram characteristics are adopted to track pedestrian based on particle filter. Firstly, the pedestrian is detected using the HOG features to determine the initial target position. Then the target is tracked based on particle filter utilizing color histogram, during which the HOG is used to modify particle heavy weights and particle sampling. Experimental results verify the accurateness and efficiency of the proposed method.

Image Adaptive Denoising Method Based on Compressive Sensing

2014 ◽  
Vol 635-637 ◽  
pp. 993-996
Author(s):  
Lin Zhang ◽  
Xia Ling Zeng ◽  
Sun Li
Keyword(s):  
Genetic Algorithm ◽  
Image Quality ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Experimental Results ◽  
New Method ◽  
Denoising Method ◽  
Edge Information ◽  
Fast Speed ◽  
Adaptive Denoising

We present a new adaptive denosing method using compressive sensing (CS) and genetic algorithm (GA). We use Regularized Orthogonal Matching Pursuit (ROMP) to remove the noise of image. ROMP algorithm has the advantage of correct performance, stability and fast speed. In order to obtain the optimal denoising effect, we determine the values of the parameters of ROMP by GA. Experimental results show that the proposed method can remove the noise of image effectively. Compared with other traditional methods, the new method retains the most abundant edge information and important details of the image. Therefore, our method has optimal image quality and a good performance on PSNR.

Real-time tracking using multiple features based on compressive sensing

2013 ◽  
Vol 21 (2) ◽  
pp. 437-444 ◽  
Author(s):  
朱秋平 ZHU Qiu-ping ◽  
颜佳 Yan Jia ◽  
张虎 Zhang Hu ◽  
范赐恩 FAN Ci-en ◽  
u邓德祥 DENG De-xiang
Keyword(s):  
Compressive Sensing ◽  
Real Time ◽  
Multiple Features ◽  
Real Time Tracking

Indoor Self-Localization Using Multiple Magnetic Sensors

2019 ◽  
Vol 31 (2) ◽  
pp. 203-211 ◽  
Author(s):  
Isaku Nagai ◽  
Jun Sakai ◽  
Keigo Watanabe ◽  
◽  
Keyword(s):  
Measurement Error ◽  
Particle Filter ◽  
Optical Sensor ◽  
Magnetic Sensors ◽  
The Self ◽  
Experimental Results ◽  
Map Matching ◽  
Multiple Sensors ◽  
Positioning Device

This study proposes an indoor self-localization for the estimation of the position and posture of an instrument using multiple magnetic sensors. First, a magnetic map for the localization is efficiently created using multiple sensors and a local positioning device made from an optical sensor and a gyroscope. For the localization estimating trajectories, the measurement error of the local positioning is corrected by matching it with the magnetic map. Our instrument is composed of six magnetic sensors, and the description of the self-localization details is based on the framework of a particle filter. The experimental results show better indoor path trajectories compared with a raw trajectory without map matching. The accuracy of the instrument using various numbers of magnetic sensors for the estimation is also investigated.

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