An Adaptive KLT Algorithm for Hand Gesture Tracking

2013 ◽  
Vol 706-708 ◽  
pp. 623-628
Author(s):  
Huang Xin
Keyword(s):  
Human Computer Interaction ◽  
State Of The Art ◽  
Tracking System ◽  
Adaptive Method ◽  
Experimental Results ◽  
Hand Gesture ◽  
Feature Points ◽  
Tracking Algorithms ◽  
Gesture Tracking ◽  
Conventional Methods

With the development of human-computer interaction technology, hand gesture is widely investigated recently for its natural and convenient properties. In view of the disadvantage of the existing tracking algorithms for the hand gesture, a novel adaptive method based on KLT is proposed in this paper, in which a kind of filtering mechanism is applied to decrease the effects of noise and illumination on tracking system. In order to eliminate the error of tracking, the strategy based on confidence is utilized properly. However, because the hand is non-rigid, its shape often changes, which easily leads to tracking failure for the reduction of features. In order to solve the problem, a method for appending the feature points is introduced. Experimental results indicate that the method presented in this paper is state of the art robustness in our comparison with related work and demonstrate improved generalization over the conventional methods.

An Improved Algorithm on Adaptive KLT Vision Tracking

2013 ◽  
Vol 631-632 ◽  
pp. 1270-1275
Author(s):  
Yuan Min Liu ◽  
Lian Fang Tian
Keyword(s):  
State Of The Art ◽  
Tracking System ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Feature Points ◽  
Backward Error ◽  
Tracking Method ◽  
Adaptive Tracking ◽  
Conventional Methods ◽  
Measurement Validity

In view of the shortage of the KLT (Kanade-Lucas-Tomasi) tracking algorithm, an improved adaptive tracking method based on KLT is proposed in this paper, in which a kind of filtering mechanism is applied to decrease the effects of noise and illumination on tracking system. In order to eliminate the error of tracking, the strategies based on forward-backward error and measurement validity are utilized properly. However, because the approach to forward-backward error makes the feature points reduce, which leads to tracking failure especially when the shapes of object change, a method for appending the feature points is introduced. Experimental results indicate that the method presented in this paper is state of the art robustness in our comparison with related work and demonstrate improved generalization over the conventional methods.

scholarly journals An Adaptive Superpixel Based Hand Gesture Tracking and Recognition System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hong-Min Zhu ◽  
Chi-Man Pun
Keyword(s):  
Template Matching ◽  
Tracking System ◽  
Recognition System ◽  
Appearance Model ◽  
Hand Gesture ◽  
Svm Classifier ◽  
Motion Trajectory ◽  
Hand Motion ◽  
Target Hand ◽  
Gesture Tracking

We propose an adaptive and robust superpixel based hand gesture tracking system, in which hand gestures drawn in free air are recognized from their motion trajectories. First we employed the motion detection of superpixels and unsupervised image segmentation to detect the moving target hand using the first few frames of the input video sequence. Then the hand appearance model is constructed from its surrounding superpixels. By incorporating the failure recovery and template matching in the tracking process, the target hand is tracked by an adaptive superpixel based tracking algorithm, where the problem of hand deformation, view-dependent appearance invariance, fast motion, and background confusion can be well handled to extract the correct hand motion trajectory. Finally, the hand gesture is recognized by the extracted motion trajectory with a trained SVM classifier. Experimental results show that our proposed system can achieve better performance compared to the existing state-of-the-art methods with the recognition accuracy 99.17% for easy set and 98.57 for hard set.

A New Algorithm of Image Matching Combining Sift and Shape Context

2014 ◽  
Vol 644-650 ◽  
pp. 4307-4313
Author(s):  
Yan Lu Xu ◽  
Yan Ma ◽  
Shun Bao Li ◽  
Ning Li Zhang ◽  
Xiang Fen Zhang ◽  
...  
Keyword(s):  
Image Matching ◽  
Experimental Results ◽  
Feature Points ◽  
Shape Context ◽  
Matching Accuracy ◽  
Conventional Methods ◽  
Distance Formula

This paper presents a new algorithm of image matching via combining sift and shape context for improving image matching accuracy. A joint descriptor is applied to describe feature points. The initial matching is obtained by a proposed distance formula. Furthermore, PLS is introduced to eliminate mismatched points. Experimental results demonstrate the proposed algorithm can achieve better performance compared to conventional methods.

scholarly journals Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification

2020 ◽  
Vol 34 (07) ◽  
pp. 12144-12151
Author(s):  
Guan-An Wang ◽  
Tianzhu Zhang ◽  
Yang Yang ◽  
Jian Cheng ◽  
Jianlong Chang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Experimental Results ◽  
Fine Grained ◽  
Invariant Features ◽  
Proposed Model ◽  
Art Methods ◽  
Conventional Methods

RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations between RGB and IR images. The key solution is to learn aligned features to the bridge RGB and IR modalities. However, due to the lack of correspondence labels between every pair of RGB and IR images, most methods try to alleviate the variations with set-level alignment by reducing the distance between the entire RGB and IR sets. However, this set-level alignment may lead to misalignment of some instances, which limits the performance for RGB-IR Re-ID. Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. Our proposed method enjoys several merits. First, our method can perform set-level alignment by disentangling modality-specific and modality-invariant features. Compared with conventional methods, ours can explicitly remove the modality-specific features and the modality variation can be better reduced. Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged images. With them, we can directly perform instance-level alignment by minimizing distances of every pair of images. Extensive experimental results on two standard benchmarks demonstrate that the proposed model favourably against state-of-the-art methods. Especially, on SYSU-MM01 dataset, our model can achieve a gain of 9.2% and 7.7% in terms of Rank-1 and mAP. Code is available at https://github.com/wangguanan/JSIA-ReID.

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

scholarly journals Automatic Detection of Discrimination Actions from Social Images

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 325
Author(s):  
Zhihao Wu ◽  
Baopeng Zhang ◽  
Tianchen Zhou ◽  
Yan Li ◽  
Jianping Fan
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Automatic Detection ◽  
Experimental Results ◽  
Practical Approach ◽  
Detection And Identification ◽  
Art Methods ◽  
Image Set ◽  
Social Images ◽  
Relationship Identification

In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches.

scholarly journals Examination of Driver Visual and Cognitive Responses to Billboard Elicited Passive Distraction Using Eye-Fixation Related Potential

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1471
Author(s):  
Yongxiang Wang ◽  
William Clifford ◽  
Charles Markham ◽  
Catherine Deegan
Keyword(s):  
Visual Information ◽  
Driving Simulator ◽  
Tracking System ◽  
Eye Gaze ◽  
Driver Distraction ◽  
Experimental Results ◽  
Human Information Processing ◽  
Cognitive Responses ◽  
Information Processing Model ◽  
Eye Fixation

Distractions external to a vehicle contribute to visual attention diversion that may cause traffic accidents. As a low-cost and efficient advertising solution, billboards are widely installed on side of the road, especially the motorway. However, the effect of billboards on driver distraction, eye gaze, and cognition has not been fully investigated. This study utilises a customised driving simulator and synchronised electroencephalography (EEG) and eye tracking system to investigate the cognitive processes relating to the processing of driver visual information. A distinction is made between eye gaze fixations relating to stimuli that assist driving and others that may be a source of distraction. The study compares the driver’s cognitive responses to fixations on billboards with fixations on the vehicle dashboard. The measured eye-fixation related potential (EFRP) shows that the P1 components are similar; however, the subsequent N1 and P2 components differ. In addition, an EEG motor response is observed when the driver makes an adjustment of driving speed when prompted by speed limit signs. The experimental results demonstrate that the proposed measurement system is a valid tool in assessing driver cognition and suggests the cognitive level of engagement to the billboard is likely to be a precursor to driver distraction. The experimental results are compared with the human information processing model found in the literature.

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