Efficient Vision-Based Calibration for Visual Surveillance Systems with Multiple PTZ Cameras

2006 ◽  
Author(s):  
I-Hsien Chen ◽  
Sheng-Jyh Wang
Keyword(s):  
Visual Surveillance ◽  
Surveillance Systems

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.

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.

scholarly journals A Conceptual Legal Framework for privacy, accountability and transparency in visual surveillance systems.

2011 ◽  
Vol 8 (4) ◽  
pp. 455-470 ◽  
Author(s):  
Nick Taylor
Keyword(s):  
Human Rights ◽  
United Kingdom ◽  
Video Surveillance ◽  
Legal Framework ◽  
Visual Surveillance ◽  
European Convention ◽  
Surveillance Systems ◽  
The United Kingdom

The United Kingdom uses visual surveillnace techniques on a huge scale, but its rewgulation of those techniques has been sadly lacking. This paper seeks to consider the extent to which the European Convention on Human Rights (ECHR) provides an overarching framework for the regulation of visual surveillance practices, both overt and covert, thereby bringing about the conditions for accountability and transparency, and to critically analyse the extent to which UK law operates within that framework so far as it applies to video surveillance.

A NOVEL FEATURE EXTRACTION SCHEME FOR HUMAN GAIT RECOGNITION

2010 ◽  
Vol 10 (04) ◽  
pp. 575-587 ◽  
Author(s):  
HAMED NASSAR ◽  
GHADA EL-TAWEEL ◽  
EMAN MAHMOUD
Keyword(s):  
Gait Recognition ◽  
Principal Component ◽  
Feature Space ◽  
Visual Surveillance ◽  
Human Gait ◽  
Surveillance Systems ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Extraction Scheme ◽  
Increasing Demand

With the increasing demand of visual surveillance systems, human recognition at a distance has gained extensive research interest. Gait is a potential behavioral feature to identify humans based on their motion. This paper describes a new scheme for extracting and selecting features from the gait of a human for recognition. The scheme combines both Key Fourier Descriptors (KFDs) and principal component analysis (PCA) techniques. This leads to a strength in reducing feature space by KFD, and increasing accuracy by PCA. Also, it is shown that the proposed scheme leads to a higher correct classification rate than schemes that depend on KFD alone or PCA alone.

Automated Object Detection and Tracking for Intelligent Visual Surveillance Based on Sensor Network

2011 ◽  
pp. 1263-1284
Author(s):  
Ruth Aguilar-Ponce ◽  
Ashok Kumar ◽  
J. Luis Tecpanecatl-Xihuitl ◽  
Magdy Bayoumi ◽  
Mark Radle
Keyword(s):  
Wireless Sensor Network ◽  
Object Detection ◽  
Sensor Network ◽  
Tracking System ◽  
Visual Surveillance ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Automated Object Detection

The aim of this research was to apply an agent approach to wireless sensor network in order to construct a distributed, automated scene surveillance. Wireless sensor network using visual nodes is used as a framework for developing a scene understanding system to perform smart surveillance. Current methods of visual surveillance depend on highly train personnel to detect suspicious activity. However, the attention of most individuals degrades after 20 minutes of evaluating monitor-screens. Therefore current surveillance systems are prompt to failure. An automated object detection and tracking was developed in order to build a reliable visual surveillance system. Object detection is performed by means of a background subtraction technique known as Wronskian change detection. After discovery, a multi-agent tracking system tracks and follows the movement of each detected object. The proposed system provides a tool to improve the reliability and decrease the cost related to the personnel dedicated to inspect the monitor-screens

On Using Gait Biometrics for Re-Identification in Automated Visual Surveillance

2017 ◽  
pp. 140-163
Author(s):  
Imed Bouchrika
Keyword(s):  
Rapid Growth ◽  
Critical Factor ◽  
Modern Society ◽  
Gait Pattern ◽  
Visual Surveillance ◽  
Surveillance Systems ◽  
Research Institutions ◽  
Non Invasive ◽  
Surveillance Technology ◽  
Gait Biometrics

As surveillance becomes ubiquitous in such modern society due to the immense increase of crimes and the rise of terrorism activities, various government and military funded projects are devoted to research institutions to work on improving surveillance technology for the safety of their citizens. Because of the rapid growth of security cameras and impossibility of manpower to supervise them, the integration of biometric technologies into surveillance systems would be a critical factor for the automation of identity tracking over distributed cameras with disjoint views i.e. Re-Identification. The interest of using gait biometrics to re-identify people over networks of cameras emerges from the fact that the gait pattern can be captured and perceived at a distance as well as its non-invasive and less-intrusive nature.

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