Multi-View Structural Local Subspace Tracking

Guo, Jie; Xu, Tingfa; Shi, Guokai; Rao, Zhitao; Li, Xiangmin

doi:10.3390/s17040666

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Another important property of CSP features which make it work so well in two-class event classification is the concept of maximal oriented energy. Since CSP is a subspace-based technique, most of the feature energy is compressed in the first few singular vectors of the CSP feature matrix, Z [41,42,43,44]. Figure 8 shows the plot of the energy content in the first 25 singular vectors of matrix Z .…”

Section: Resultsmentioning

confidence: 99%

Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

Yahya

Musa

Ong

et al. 2019

Sensors

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In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.

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Section: Resultsmentioning

confidence: 99%

Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

Yahya

Musa

Ong

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Even though existing tracking algorithms using hand-crafted features can achieve satisfactory results [ 38 , 39 ], applying deep features to pedestrian tracking is a good choice to improve the performance. To make features more discriminative, metric learning is the appropriate measure when we apply deep features, due to the uncertainty of pedestrian numbers and the similarity between pedestrians.…”

Section: Related Workmentioning

confidence: 99%

NCA-Net for Tracking Multiple Objects across Multiple Cameras

Tan

Tai

Xiong

2018

Sensors

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Tracking multiple pedestrians across multi-camera scenarios is an important part of intelligent video surveillance and has great potential application for public security, which has been an attractive topic in the literature in recent years. In most previous methods, artificial features such as color histograms, HOG descriptors and Haar-like feature were adopted to associate objects among different cameras. But there are still many challenges caused by low resolution, variation of illumination, complex background and posture change. In this paper, a feature extraction network named NCA-Net is designed to improve the performance of multiple objects tracking across multiple cameras by avoiding the problem of insufficient robustness caused by hand-crafted features. The network combines features learning and metric learning via a Convolutional Neural Network (CNN) model and the loss function similar to neighborhood components analysis (NCA). The loss function is adapted from the probability loss of NCA aiming at object tracking. The experiments conducted on the NLPR_MCT dataset show that we obtain satisfactory results even with a simple matching operation. In addition, we embed the proposed NCA-Net with two existing tracking systems. The experimental results on the corresponding datasets demonstrate that the extracted features using NCA-net can effectively make improvement on the tracking performance.

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“…Liu et al [20] use a local sparse representation for representing the target and exploit the sparse coding histogram to represent the dynamic dictionary basis distribution of the target model. Guo et al [21] propose a novel multi-view structural local subspace method which jointly exploits the advantages of three sub-models and uses an alignment-weighting average method to obtain the optimal state of the target. Wang et al [22] adopt squared templates to replace trivial templates to handle partial occlusion and propose a probabilistic collaborative representation framework, which reduces the complexity in traditional sparse model-based methods.…”

Section: Introductionmentioning

confidence: 99%

Robust L1 tracker with CNN features

Wang

2017

J Wireless Com Network

Self Cite

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Recently, L1 tracker has been widely applied and received great success in visual tracking. However, most L1 trackers use only the image intensity for sparse representation, which is insufficient to represent the object especially when drastic appearance changes occur. Convolutional neural network (CNN) has demonstrated remarkable capability in a wide range of computer vision fields, and features extracted from different convolutional layers have different characteristics. In this paper, we propose a novel sparse representation model with convolutional features for visual tracking. Besides, to alleviate the redundancy from high-dimensional convolutional features, a feature selection method is adopted to remove noisy and irrelevant feature maps, which can reduce computation redundancy and improve tracking accuracy. Different from traditional sparse representation based tracking methods, our model not only exploits convolutional features to improve the robustness for describing the object appearance but also uses the trivial templates to model both reconstruction errors caused by sparse representation and the eigensubspace representation. In addition, an unified objective function is proposed and a customized APG method is developed to effectively solve the optimization problem. Numerous qualitative and quantitative evaluations demonstrate that our tracker outperforms other state-of-the-art trackers in a wide range of tracking scenarios.

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Multi-View Structural Local Subspace Tracking

Cited by 5 publications

References 33 publications

Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

NCA-Net for Tracking Multiple Objects across Multiple Cameras

Robust L1 tracker with CNN features

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