Automatic emotional state detection using facial expression dynamic in videos

In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states. The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems

Automatic Intruder Combat System: A way to Smart Border Surveillance

Security and safeguard of international borders have always been a dominant issue for every nation. A large part of a nation’s budget is provided to its defense system. Besides wars, illegal intrusion in terms of terrorism is a critical matter that causes severe harm to nation’s property. In India’s perspective, border patrolling by Border Security Forces (BSF) has already been practiced from a long time for surveillance. The patrolling parties are equipped with high-end surveillance equipments but yet an alternative to the ply of huge manpower and that too in harsh environmental conditions hasn’t been in existence. An automatic mechanism for smart surveillance and combat is proposed in this paper as a solution to the above-discussed problems. Smart surveillance requires automatic intrusion detection in the surveillance video, which is achieved by using optical flow information as motion features for intruder/human in the scene. The use of optical flow in the proposed smart surveillance makes it robust and more accurate. Use of a simple horizontal feature for fence detection makes system simple and faster to work in real-time. System is also designed to respond against the activities of intruders, of which auto combat is one kind of response

Design and realization of motion detector system for house security

In this paper, the design and realization of motion detector system for house security based GSM network is presents. The development of microcontroller carried out intruder detection that supports tracking techniques to provide vital security with control and alert operation inside and outside the home. The pivot of security on the integration the motion detector and cameras into web applications has become more interested. The smart surveillance Pi camera obtain the input from the motion detector and controller which is send the video to the web server allowing the homeowner to access this video by use web applications. An intrusion alert send to the owner by mean of message via mobile and buzzers alarms located at suitable distance. This system is typify proficient video camera for remote sensing and tracking with live video for succeeding play again to offers efficient and easy implementation with omnipresent surveillance solutio

An intelligent surveillance platform for large metropolitan areas with dense sensor deployment

Producción CientíficaThis paper presents an intelligent surveillance platform based on the usage of large numbers of inexpensive sensors designed and developed inside the European Eureka Celtic project HuSIMS. With the aim of maximizing the number of deployable units while keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is based on the usage of inexpensive visual sensors which apply efficient motion detection and tracking algorithms to transform the video signal in a set of motion parameters. In order to automate the analysis of the myriad of data streams generated by the visual sensors, the platform’s control center includes an alarm detection engine which comprises three components applying three different Artificial Intelligence strategies in parallel. These strategies are generic, domain-independent approaches which are able to operate in several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The architecture is completed with a versatile communication network which facilitates data collection from the visual sensors and alarm and video stream distribution towards the emergency teams. The resulting surveillance system is extremely suitable for its deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap visual sensors and autonomous alarm detection facilitate dense sensor network deployments for wide and detailed coveraMinisterio de Industria, Turismo y Comercio and the Fondo de Desarrollo Regional (FEDER) and the Israeli Chief Scientist Research Grant 43660 inside the European Eureka Celtic project HuSIMS (TSI-020400-2010-102)

Autonomous real-time surveillance system with distributed IP cameras

An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator

An Efficient GA Based Detection Approach for Visual Surveillance System

ABSTRACT: Now-a-days, for an intelligent surveillance system, identification of an object from a video has attracted a great deal of interest. To detect the object from a video one need to perform some segmentation techniques. In real time application, Object segmentation and identification are two essential building block of smart surveillance system. In addition, some conditions make video object detection difficult such as non rigid object motion, target appearance variations due to changes in illumination and background clutter. This method is proposed on a multi object moving background based on Genetic algorithm. The video is preprocessed before segmentation. Motion segmentation is done to segment an object from a video. For motion detection, a genetic algorithm is used.In this, a Non maximum suppression filter is proposed to remove the unwanted object motion. This result is then used for object identification. Cellular automata based segmentation is performed to detect a particular object from a video. This method can detect any object at any drastic change in illumination