scholarly journals WePBAS: A Weighted Pixel-Based Adaptive Segmenter for Change Detection

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2672
Author(s):  
Wenhui Li ◽  
Jianqi Zhang ◽  
Ying Wang
Keyword(s):  
Change Detection ◽  
High Weight ◽  
State Of The Art ◽  
Diffusion Mechanism ◽  
Background Modeling ◽  
Background Model ◽  
Dynamic Background ◽  
Low Weight ◽  
Model Update ◽  
Sample Set

The pixel-based adaptive segmenter (PBAS) is a classic background modeling algorithm for change detection. However, it is difficult for the PBAS method to detect foreground targets in dynamic background regions. To solve this problem, based on PBAS, a weighted pixel-based adaptive segmenter named WePBAS for change detection is proposed in this paper. WePBAS uses weighted background samples as a background model. In the PBAS method, the samples in the background model are not weighted. In the weighted background sample set, the low-weight background samples typically represent the wrong background pixels and need to be replaced. Conversely, high-weight background samples need to be preserved. According to this principle, a directional background model update mechanism is proposed to improve the segmentation performance of the foreground targets in the dynamic background regions. In addition, due to the “background diffusion” mechanism, the PBAS method often identifies small intermittent motion foreground targets as background. To solve this problem, an adaptive foreground counter was added to the WePBAS to limit the “background diffusion” mechanism. The adaptive foreground counter can automatically adjust its own parameters based on videos’ characteristics. The experiments showed that the proposed method is competitive with the state-of-the-art background modeling method for change detection.

scholarly journals Background Modeling with Extracted Dynamic Pixels for Pumping Unit Surveillance

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Tianming Yu ◽  
Jianhua Yang ◽  
Wei Lu
Keyword(s):  
State Of The Art ◽  
Background Modeling ◽  
Object Motion ◽  
Dynamic Scenes ◽  
Complex Dynamic ◽  
Dynamic Background ◽  
Sample Distribution ◽  
Modeling Methods ◽  
Monitoring Applications ◽  
Pumping Unit

Background modeling plays an important role in the application of intelligent video surveillance. Researchers have presented diverse approaches to support the development of dynamic background modeling. However, in the case of pumping unit surveillance, traditional background modeling methods often mistakenly detect the periodic rotational pumping unit as the foreground object. To address this problem here, we propose a novel background modeling method for foreground segmentation, particularly in dynamic scenes that include a rotational pumping unit. In the proposed method, the ViBe method is employed to extract possible foreground pixels from the sequence frames and then segment the video image into dynamic and static regions. Subsequently, the kernel density estimation (KDE) method is used to build a background model with dynamic samples of each pixel. The bandwidth and threshold of the KDE model are calculated according to the sample distribution and extremum of each dynamic pixel. In addition, the strategy of sample adjustment combines regular and real-time updates. The performance of the proposed method is evaluated against several state-of-the-art methods applied to complex dynamic scenes consisting of a rotational pumping unit. Experimental results show that the proposed method is available for periodic object motion scenario monitoring applications.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

scholarly journals Correlation Tracking via Self-Adaptive Fusion of Multiple Features

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 241 ◽  
Author(s):  
Zhi Chen ◽  
Peizhong Liu ◽  
Yongzhao Du ◽  
Yanmin Luo ◽  
Wancheng Zhang
Keyword(s):  
State Of The Art ◽  
Correlation Filter ◽  
Multiple Features ◽  
Multi Scale ◽  
Tracking Algorithms ◽  
Model Update ◽  
Adaptive Fusion ◽  
Tracking Model ◽  
Update Strategy ◽  
Self Adaptive

Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods.

scholarly journals CHANGE DETECTION AND DEFORMATION ANALYSIS BASED ON MOBILE LASER SCANNING DATA OF URBAN AREAS

2020 ◽  
Vol V-2-2020 ◽  
pp. 703-710
Author(s):  
J. Gehrung ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Laser Scanning ◽  
State Of The Art ◽  
Point Clouds ◽  
Deformation Analysis ◽  
Lidar Data ◽  
Efficient Manner ◽  
Incomplete Observations ◽  
Novel Method

Abstract. Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.

scholarly journals Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yizhong Yang ◽  
Qiang Zhang ◽  
Pengfei Wang ◽  
Xionglou Hu ◽  
Nengju Wu
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model ◽  
Dynamic Background ◽  
Spatiotemporal Model ◽  
Temporal Features ◽  
Common Technique ◽  
Public Datasets

Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.

scholarly journals Scalable Bi-Directional SMA-Based Rotational Actuator

Actuators ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 60 ◽  
Author(s):  
Rouven Britz ◽  
Paul Motzki ◽  
Stefan Seelecke
Keyword(s):  
Modular Design ◽  
High Weight ◽  
State Of The Art ◽  
Industrial Applications ◽  
Combustion Engines ◽  
Rotational Angle ◽  
Output Torque ◽  
Rotational Actuator ◽  
Rotatory Motion ◽  
Total Angle

In industrial applications, rotatory motions and torques are often needed. State-of-the-art actuators are based on either combustion engines, electro-motors, hydraulic, or pneumatic machines. The main disadvantages are the construction space, the high weight, and a large amount of needed peripheral devices. To overcome these limitations, compact and light-weight actuator systems can be built by using shape memory alloys (SMAs), which are known for their superior energy density. In this paper, the development of a scalable bi-directional rotational actuator based on SMA wires is presented. The scalability was based on a modular design, which allowed the actuator to be adapted to various application specifications by customizing the rotational angle and the output torque. On the mechanical side, each module enabled a small rotatory motion, which added up to the total angle of the actuator. The SMA wires were arranged in an agonist-antagonist configuration to provide active rotation in both directions. The presented prototype achieved a total rotation of 100°. The modularity of the mechanical concept is also reflected in the electronics, which is discussed in this paper as well. This consideration allows the electronics to be adapted to the mechanics with minimal changes. As a result, a prototype, including the presented mechanical and electronic design, is reported in this study.

scholarly journals Coat colour in mouse populations selected for weight gain: support for hitchhiking, not pleiotropy

2013 ◽  
Vol 95 (1) ◽  
pp. 4-13 ◽  
Author(s):  
PHILIP W. HEDRICK
Keyword(s):  
Weight Gain ◽  
Allele Frequency ◽  
High Weight ◽  
Coat Colour ◽  
Correlated Response ◽  
Term Selection ◽  
Low Weight ◽  
High Weight Gain ◽  
Frequency Changes ◽  
Positive Correlations

SummaryWith many molecular markers in many species, research efforts in quantitative genetics have focused on dissecting these traits and understanding the importance of factors such as correlated response due to hitchhiking or pleiotropy. Here, in an examination of long-term selection experiments in mice, the evidence strongly supports the primary importance of hitchhiking on the coat colour loci brown and dilute in mice selected for high weight gain. First, the amount of observed change in coat colour allele frequency could not be explained by genetic drift alone, implying that selection was of high importance. Second, the allele frequency changes included reversals in the direction change, but there were still positive correlations in the early generations with differences in weight gain between the phenotypes. Third, the correlation between the change in allele frequencies and phenotypic difference in weight gain declined over time, consistent with the decay expected from linkage associations. Fourth, the changes at both loci in a short-term selection experiment for low weight gain were in the opposite direction than the changes in the contemporaneous related population selected for high weight gain.

scholarly journals Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

2019 ◽  
Vol 11 ◽  
pp. 175682931882232
Author(s):  
Navid Dorudian ◽  
Stanislao Lauria ◽  
Stephen Swift
Keyword(s):  
Background Subtraction ◽  
High Speed ◽  
Micro Air Vehicles ◽  
Background Model ◽  
Depth Information ◽  
Subtraction Technique ◽  
Depth Images ◽  
Novel Approach ◽  
Model Update ◽  
Air Vehicles

A novel approach to detect micro air vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving micro air vehicles by combining colour and depth information. The proposed method stores several colour and depth images as models and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background model update presented uses different criteria from existing methods. Additionally, a blind update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different micro air vehicles and publicly available datasets. Results showing some improvements over existing methods are discussed.

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