A Calibration Algorithm for Multi-camera Visual Surveillance Systems Based on Single-View Metrology

2015 ◽  
pp. 552-559 ◽  
Author(s):  
J. C. Neves ◽  
J. C. Moreno ◽  
S. Barra ◽  
H. Proença
Keyword(s):  
Visual Surveillance ◽  
Surveillance Systems ◽  
Single View ◽  
Calibration Algorithm ◽  
Single View Metrology

Learning Algorithms for Anomaly Detection from Images

Biometrics ◽  
2017 ◽  
pp. 281-308
Author(s):  
Tarem Ahmed ◽  
Al-Sakib Khan Pathan ◽  
Supriyo Shafkat Ahmed
Keyword(s):  
Human Security ◽  
Visual Surveillance ◽  
Automated System ◽  
Surveillance Systems ◽  
Human Beings ◽  
Security Officer ◽  
Security Officers ◽  
Large Numbers ◽  
Quantitative Performance ◽  
Present World

Visual surveillance networks are installed in many sensitive places in the present world. Human security officers are required to continuously stare at large numbers of monitors simultaneously, and for lengths of time at a stretch. Constant alert vigilance for hours on end is difficult to maintain for human beings. It is thus important to remove the onus of detecting unwanted activity from the human security officer to an automated system. While many researchers have proposed solutions to this problem in the recent past, significant gaps remain in existing knowledge. Most existing algorithms involve high complexities. No quantitative performance analysis is provided by most researchers. Most commercial systems require expensive equipment. This work proposes algorithms where the complexities are independent of time, making the algorithms naturally suited to online use. In addition, the proposed methods have been shown to work with the simplest surveillance systems that may already be publicly deployed. Furthermore, direct quantitative performance comparisons are provided.

Study on fabric compression under seam vis a vis quantitative measurement of seam boldness and thread consumption for lockstitch 301

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Md Vaseem Chavhan ◽  
Mandapati Ramesh Naidu
Keyword(s):  
Quantitative Measurement ◽  
Multilinear Regression ◽  
New Approach ◽  
Content Type ◽  
Single View ◽  
Sewing Thread ◽  
Stitch Density ◽  
Proposed Model ◽  
Fabric Weight ◽  
Single View Metrology

Purpose This paper aims to develop at sewing thread during the seam formation may lead to the compression of fabric under seam. In the present study, the model has been proposed to predict the seam compression and calculation of seam boldness, as well as thread consumption by considering seam compression. Design/methodology/approach The effect of sewing parameters on the fabric compression at the seam (Cf) for fabrics of varying bulk density was studied by the Taguchi method and also the multilinear regression equation is obtained to predict seam compression by considering these parameters. The framework has been set as per the single view metrology approach to measuring structural seam boldness (Bs). One of the basic geometrical models (Ghosh and Chavhan, 2014) for the prediction of thread consumption at lock stitch has been modified by considering fabric compression at the seam (Cf). Findings The multilinear regression model has been proposed which can predict the compression under seam using easily measurable fabric parameters and stitch density. The seam boldness is successfully calculated quantitatively using the proposed formula with a good correlation with the seam boldness rated subjectively. The thread consumption estimation from the proposed approach was found to be more accurate. Originality/value The compression under seam is found out using easily measurable parameters; fabric thickness, fabric weight and stitch density from the proposed model. The attempt has been made to calculate seam boldness quantitatively and the new approach to find out thread consumption by considering the seam compression has been proposed.

scholarly journals Estimating Body Related Soft Biometric Traits in Video Frames

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Olasimbo Ayodeji Arigbabu ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Salman Yussof ◽  
Vahab Iranmanesh ◽  
...  
Keyword(s):  
Human Subjects ◽  
Visual Surveillance ◽  
Frame Rate ◽  
Visual Appearance ◽  
Single View ◽  
Soft Biometrics ◽  
Related Information ◽  
Video Frames ◽  
Low Frame Rate ◽  
Biometric Information

Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information. This paper presents an approach for estimating body related soft biometrics; specifically we propose a new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate. Our evaluation on 1120 frame sets of 80 subjects from a newly compiled dataset shows that the mentioned soft biometric information of human subjects can be adequately predicted from set of frames.

ANALYSIS OF HUMAN WALKING TRAJECTORIES FOR SURVEILLANCE

2004 ◽  
Vol 01 (02) ◽  
pp. 169-189
Author(s):  
KA KEUNG LEE ◽  
YANGSHENG XU
Keyword(s):  
Real Time ◽  
Similarity Measure ◽  
Real Life ◽  
Visual Surveillance ◽  
Longest Common Subsequence ◽  
Human Walking ◽  
Support Vector ◽  
Surveillance Systems ◽  
Boundary Values ◽  
Variable Boundary

Surveillance of public places has become a worldwide concern in recent years. The ability to identify abnormal human behaviors in real-time is fundamental to the success of intelligent surveillance systems. The recognition of abnormal and suspicious human walking patterns is an important step towards the achievement of this goal. In this research, we develop an intelligent visual surveillance system that can classify normal and abnormal human walking trajectories in outdoor environments by learning from demonstration. The system takes into account both the local and global characteristics of the observed trajectories and is able to identify their normality in real-time. By utilizing support vector learning and a similarity measure based on hidden Markov models, the developed system has produced satisfactory results on real-life data during testing. Moreover, we utilize the approach of longest common subsequence (LCSS) in determining the similarity between different types of walking trajectories. In order to establish the position and speed boundaries required for the similarity measure, we compare the performance of a number of approaches, including fixed boundary values, variable boundary values, learning boundary by support vector regression, and learning boundary by cascade neural networks.

scholarly journals Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach

2020 ◽  
Author(s):  
Vũ Hữu Tiến ◽  
Thao Nguyen Thi Huong ◽  
San Vu Van ◽  
Xiem HoangVan
Keyword(s):  
Deep Learning ◽  
Activation Function ◽  
Vital Role ◽  
Visual Surveillance ◽  
Noise Model ◽  
Perceptual Quality ◽  
Surveillance Systems ◽  
Compression Performance ◽  
Laplacian Distribution ◽  
Correlation Noise

Transform domain Wyner-Ziv video coding (TDWZ) has shown its benefits in compressing video applications with limited resources such as visual surveillance systems, remote sensing and wireless sensor networks. In TDWZ, the correlation noise model (CNM) plays a vital role since it directly affects to the number of bits needed to send from the encoder and thus the overall TDWZ compression performance. To achieve CNM with high accurate for TDWZ, we propose in this paper a novel CNM estimation approach in which the CNM with Laplacian distribution is adaptively estimated based on a deep learning (DL) mechanism. The proposed DL based CNM includes two hidden layers and a linear activation function to adaptively update the Laplacian parameter. Experimental results showed that the proposed TDWZ codec significantly outperforms the relevant benchmarks, notably by around 35% bitrate saving when compared to the DISCOVER codec and around 22% bitrate saving when compared to the HEVC Intra benchmark while providing a similar perceptual quality.

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