scholarly journals Active Shift Attention Based Object Tracking System

2021 ◽  
Author(s):  
◽  
Aisha Ajmal
Keyword(s):  
Object Tracking ◽  
Computational Models ◽  
Vision System ◽  
Tracking System ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Relevant Information ◽  
Human Vision ◽  
Biological Mechanisms ◽  
Salient Regions

<p>The human vision system (HVS) collects a huge amount of information and performs a variety of biological mechanisms to select relevant information. Computational models based on these biological mechanisms are used in machine vision to select interesting or salient regions in the images for application in scene analysis, object detection and object tracking.  Different object tracking techniques have been proposed often using complex processing methods. On the other hand, attention-based computational models have shown significant performance advantages in various applications. We hypothesise the integration of a visual attention model with object tracking can be effective in increasing the performance by reducing the detection complexity in challenging environments such as illumination change, occlusion, and camera moving.  The overall objective of this thesis is to develop a visual saliency based object tracker that alternates between targets using a measure of current uncertainty derived from a Kalman filter. This thesis presents the results by showing the effectiveness of the tracker using the mean square error when compared to a tracker without the uncertainty mechanism.   Specific colour spaces can contribute to the identification of salient regions. The investigation is done between the non-uniform red, green and blue (RGB) derived opponencies with the hue, saturation and value (HSV) colour space using video information. The main motivation for this particular comparison is to improve the quality of saliency detection in challenging situations such as lighting changes. Precision-Recall curves are used to compare the colour spaces using pyramidal and non-pyramidal saliency models.</p>

scholarly journals Active Shift Attention Based Object Tracking System

2021 ◽  
Author(s):  
◽  
Aisha Ajmal
Keyword(s):  
Object Tracking ◽  
Computational Models ◽  
Vision System ◽  
Tracking System ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Relevant Information ◽  
Human Vision ◽  
Biological Mechanisms ◽  
Salient Regions

<p>The human vision system (HVS) collects a huge amount of information and performs a variety of biological mechanisms to select relevant information. Computational models based on these biological mechanisms are used in machine vision to select interesting or salient regions in the images for application in scene analysis, object detection and object tracking.  Different object tracking techniques have been proposed often using complex processing methods. On the other hand, attention-based computational models have shown significant performance advantages in various applications. We hypothesise the integration of a visual attention model with object tracking can be effective in increasing the performance by reducing the detection complexity in challenging environments such as illumination change, occlusion, and camera moving.  The overall objective of this thesis is to develop a visual saliency based object tracker that alternates between targets using a measure of current uncertainty derived from a Kalman filter. This thesis presents the results by showing the effectiveness of the tracker using the mean square error when compared to a tracker without the uncertainty mechanism.   Specific colour spaces can contribute to the identification of salient regions. The investigation is done between the non-uniform red, green and blue (RGB) derived opponencies with the hue, saturation and value (HSV) colour space using video information. The main motivation for this particular comparison is to improve the quality of saliency detection in challenging situations such as lighting changes. Precision-Recall curves are used to compare the colour spaces using pyramidal and non-pyramidal saliency models.</p>

Salient Region Detection for Biometric Watermarking

2011 ◽  
pp. 218-236
Author(s):  
Ma Bin ◽  
Li Chun-lei ◽  
Wang Yun-hong ◽  
Bai Xiao
Keyword(s):  
Vision System ◽  
Region Of Interest ◽  
Visual Saliency ◽  
Saliency Map ◽  
Human Vision ◽  
Identification Accuracy ◽  
Biometric Data ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Visual saliency, namely the perceptual significance to human vision system (HVS), is a quality that differentiates an object from its neighbors. Detection of salient regions which contain prominent features and represent main contents of the visual scene, has obtained wide utilization among computer vision based applications, such as object tracking and classification, region-of-interest (ROI) based image compression, etc. Specially, as for biometric authentication system, whose objective is to distinguish the identification of people through biometric data (e.g. fingerprint, iris, face etc.), the most important metric is distinguishability. Consequently, in biometric watermarking fields, there has been a great need of good metrics for feature prominency. In this chapter, we present two salient-region-detection based biometric watermarking scenarios, in which robust annotation and fragile authentication watermark are respectively applied to biometric systems. Saliency map plays an important role of perceptual mask that adaptively select watermarking strength and position, therefore controls the distortion introduced by watermark and preserves the identification accuracy of biometric images.

Salient Region Detection for Biometric Watermarking

2013 ◽  
pp. 201-219
Author(s):  
Bin Ma ◽  
Chun-lei Li ◽  
Yun-hong Wang ◽  
Xiao Bai
Keyword(s):  
Vision System ◽  
Region Of Interest ◽  
Visual Saliency ◽  
Saliency Map ◽  
Human Vision ◽  
Identification Accuracy ◽  
Biometric Data ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Visual saliency, namely the perceptual significance to human vision system (HVS), is a quality that differentiates an object from its neighbors. Detection of salient regions which contain prominent features and represent main contents of the visual scene, has obtained wide utilization among computer vision based applications, such as object tracking and classification, region-of-interest (ROI) based image compression, etc. Specially, as for biometric authentication system, whose objective is to distinguish the identification of people through biometric data (e.g. fingerprint, iris, face etc.), the most important metric is distinguishability. Consequently, in biometric watermarking fields, there has been a great need of good metrics for feature prominency. In this chapter, we present two salient-region-detection based biometric watermarking scenarios, in which robust annotation and fragile authentication watermark are respectively applied to biometric systems. Saliency map plays an important role of perceptual mask that adaptively select watermarking strength and position, therefore controls the distortion introduced by watermark and preserves the identification accuracy of biometric images.

scholarly journals Saliency Aggregation: Multifeature and Neighbor Based Salient Region Detection for Social Images

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Ye Liang ◽  
Congyan Lang ◽  
Jian Yu ◽  
Hongzhe Liu ◽  
Nan Ma
Keyword(s):  
Saliency Detection ◽  
Visual Saliency ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Image Appearance ◽  
Deep Convolution Neural Network ◽  
Salient Regions ◽  
Social Images ◽  
Visual Saliency Models

The popularity of social networks has brought the rapid growth of social images which have become an increasingly important image type. One of the most obvious attributes of social images is the tag. However, the sate-of-the-art methods fail to fully exploit the tag information for saliency detection. Thus this paper focuses on salient region detection of social images using both image appearance features and image tag cues. First, a deep convolution neural network is built, which considers both appearance features and tag features. Second, tag neighbor and appearance neighbor based saliency aggregation terms are added to the saliency model to enhance salient regions. The aggregation method is dependent on individual images and considers the performance gaps appropriately. Finally, we also have constructed a new large dataset of challenging social images and pixel-wise saliency annotations to promote further researches and evaluations of visual saliency models. Extensive experiments show that the proposed method performs well on not only the new dataset but also several state-of-the-art saliency datasets.

Sparse Representation of the Human Vision Information and the Saliency Detection Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 3349-3353
Author(s):  
Ju Bo Jin ◽  
Yu Xi Liu
Keyword(s):  
Firing Rate ◽  
Sparse Representation ◽  
Large Scale ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Detection Algorithm ◽  
Human Vision ◽  
Short Term ◽  
Natural Statistics ◽  
Sparse Features

Representation and measurement are two important issues for saliency models. Different with previous works that learnt sparse features from large scale natural statistics, we propose to learn features from short-term statistics of single images. For saliency measurement, we defined basic firing rate (BFR) for each sparse feature, and then we propose to use feature activity rate (FAR) to measure the bottom-up visual saliency. The proposed FAR measure is biological plausible and easy to compute and with satisfied performance. Experiments on human trajectory positioning and psychological patterns demonstrate the effectiveness and robustness of our proposed method.

Small infrared target detection based on image patch ordering

2016 ◽  
Vol 14 (02) ◽  
pp. 1640007 ◽  
Author(s):  
Yantao Wei ◽  
Xinge You ◽  
He Deng
Keyword(s):  
Target Detection ◽  
Vision System ◽  
Tracking System ◽  
Contrast Ratio ◽  
Input Image ◽  
Human Vision ◽  
Image Patch ◽  
System A ◽  
Contrast Measure ◽  
Contrast Mechanism

Small infrared (IR) target detection is an important and challenging issue in IR target tracking system. In this paper, a patch ordering-based method is proposed for small IR target detection in an image with complicated background. Inspired by the contrast mechanism of human vision system, a patch ordering-based contrast measure (POCM) is designed to deal with the input image, which cannot only suppress the background noise and clutter but also enhance the targets significantly. In this way, POCM can increase the contrast ratio between target and background. This leads to higher Signal Clutter Ratio (SCR). Then the true target can be detected by applying simple adaptive thresholding method. The experimental results on two sequences show that the proposed method can efficiently detect small IR target from heavy clutter.

scholarly journals Addressing lead exposure in children in Georgia: challenges and successes of a multi-agency response

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
E Ruadze ◽  
I Khonelidze ◽  
L Sturua ◽  
P Lauriola ◽  
H Crabbe ◽  
...  
Keyword(s):  
Public Health ◽  
Blood Concentration ◽  
Tracking System ◽  
Action Plan ◽  
Blood Lead ◽  
Relevant Information ◽  
State Intervention ◽  
Intervention Programme ◽  
Public Health Action ◽  
Pb Exposure

Abstract The national response for reducing lead (Pb) exposure in Georgia is coordinated by the National Centre for Disease Control and Public Health (NCDC&PH) and implemented as a multi-agency (CDC, UNICEF, WHO, University of Emory) response. Given concerns about the extent of Pb exposure, in 2018 Multiple Indicator Cluster Surveys (MICS) of representative samples of children have been conducted to study several demographic and health aspects, including a study of the prevalence of blood Pb levels among 2 to 7 years old children (n = 1578). This survey was conducted in collaboration with the Italian Instituto Superiore di Sanita' (ISS), UNICEF and NCDC. The laboratory analyses were conducted at ISS in Italy. Initial results showed that in 41% of all children, blood Pb concentration was ≥ 5 µg/dl, a challenge which motivated public agencies to establish an initial public health action plan to assess environmental samples (paint, dust, water, soil, selected food items such as spices and imported sweets) in families where Pb concentrations were ≥ 10 µg/dL. A State intervention programme, monitoring Pb blood concentration among MICS children and their family members, provided relevant information on exposed households and led to a reduction of Pb blood concentration across the most exposed households. In collaboration with Public Health England, NCDC has conducted a small Pb isotope ratio study aimed at identifying the most relevant sources of Pb exposure contributing to elevated blood Pb in MICS children. It is expected that these data will support the design of more detailed public health interventions to reduce exposure to key sources of Pb, thus leading to further reduction of Pb-induced health effects in Georgia. In addition, this experience will clarify elements of an ongoing monitoring of environmental factors such as an Environmental Public Health Tracking system, to support national capacity to manage the risks to public health. Key messages Environmental health response requires extensive research and multi-agency approach. If State implements adequate intervention it is possible to reduce blood lead (Pb) level.

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