ADVANCED MOTION DETECTION ALGORITHM FOR PATIENT MONITORING USING CELL PHONE WITH VIDEO DISPLAY

Proposed is a smart, reliable and robust algorithm for motion detection, tracking and activity analysis. Background subtraction is considered intelligent algorithms for the same. We use this to track the motion and monitor the movements of the subject in question. Mount the web camera focused to the patient. PC should have a unique external Internet IPAddress. Android mobile phone should be GPRS enabled. GSM technology is used for sending SMS. It is a client-server technology wherein client captures the images, checks for motion if any, discards the packets until motion is detected. Use background subtraction algorithm to check the motion. The surveillance camera does not move and has a capture of the static background it is facing. It uses image subtraction to determine object motion. It provides more reliable information about moving object, but it is so sensitivity to the dynamic changes such as lighting. Once motion is detected, camera stops monitoring further motion. Instead, it starts capturing the video. Simultaneously, SMS alert is sent to the responsible doctors and also alerting the medical staff with audio speaker in the hospital. Java mail API is used to mail the captured video to the entered e-mail IDs. Once the doctor demands for video, socket is established between the PC and the mobile phone and video (series of images) are streamed to the doctor’s mobile phone. Save live video of first few seconds at the server end for future use. Activate alert at the remote end

Advanced Moving Object Detection and Tracking for Video Surveillance

Moving object detection is a very crucial and challenging task in computer vision applications such as surveillance, vehicle and human tracking. Background subtraction is a preliminary technique widely used for the moving object detection. In this paper, an advanced automated moving object detection technique using background subtraction is proposed. The method uses running average wavelet transform (RAWT) for accurate registration of background from the video sequence. Furthermore, the moving objects are detected by comparing current and background frame. In order to produce higher accuracy for the object detection, the proposed method also further includes post-processing filter operation after which the binary object detection mask can be obtained. After moving object detection, tracking is performed. Experimental results demonstrate that the proposed method is faster and efficient as compared to the other state-of-the-art existing methods

CORRECTING FALSE SEGMENTATION IN VIDEO USING IMAGE OVER-SEGMENTATION

Moving objects detection is a fundamental step in many vision based applications. Background subtraction is the typical method. When scene exhibits pertinent dynamism method based on mixture of Gaussians is a good balance between accuracy and complexity, but fails due to two kinds of false segmentations i.e moving shadows incorrectly detected as objects and some actual moving objects not detected as moving objects. In computer vision, segmentation refers to process of partitioning a digital image in to multiple segments and goal of segmentation is to simplify and/or change representation of image in to something that is more meaningful and easier to analyse. A colour clustering based on k-means and image over-segmentation are used to segment the input frame into patches and shadow suppression done by HSV colour space, the outputs of mixture of Gaussians are combined with the colour clustered regions to a module for area confidence measurement. In this way, two major segment errors can be corrected. Experimental results show that the proposed approach can significantly enhance segmentation results

A block-based background model for moving object detection

Detecting the moving objects in a video sequence using a stationary camera is an important task for many computer vision applications. This paper proposes a background subtraction approach. As first step, the background is initialized using the block-based analysis before being updated in each incoming frame. Our background frame is generated by collecting the blocks background candidates. The block candidate selection is based on probability density function (pdf) computation. After that, the absolute difference between the background frame and each frame of sequence is computed. A noise filter is applied using the Structure/Texture decomposition in order to minimize the noise caused by background subtraction operation. The binary motion mask is formed using an adaptive threshold that was deduced from the weighted mean and variance calculation. To assure the correspondence between the current frame and the background frame, an adaptation of background model in each incoming frame is realized. After comparing results obtained from the proposed method to other existing ones, it was shown that our approach attains a higher degree of efficac

Vehicle Detection and Tracking Techniques: A Concise Review

Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic analysis and vehicle categorizing objectives and may be implemented under different environments changes. In this review, we present a concise overview of image processing methods and analysis tools which used in building these previous mentioned applications that involved developing traffic surveillance systems. More precisely and in contrast with other reviews, we classified the processing methods under three categories for more clarification to explain the traffic systems

Segmentation of Moving Objects in Video Sequences with a Dynamic Background

Segmentation of objects from a video sequence is one of the basic operations commonly employed in vision-based systems. The quality of the segmented object has a profound effect on the performance of such systems. Segmentation of an object becomes a challenging problem in situations in which the background scenes of a video sequence are not static or contain the cast shadow of the object. This thesis is concerned with developing cost-effective methods for object segmentation from video sequences having dynamic background and cast shadows. A novel technique for the segmentation of foreground from video sequences with a dynamic background is developed. The segmentation problem is treated as a problem of classifying the foreground and background pixels of the frames of a sequence using the pixel color components as multiple features of the images. The individual features representing the pixel gray levels, hue and saturation levels are first extracted and then linearly recombined with suitable weights to form a scalar-valued feature image. Multiple features incorporated into this scalar-valued feature image allows to devise a simple classification scheme in the framework of a support vector machine classifier. Unlike some other data classification approaches for foreground segmentation, in which a priori knowledge of the shape and size of the moving foreground is essential, in the proposed method, training samples are obtained in an automated manner. The proposed technique is shown not to be limited by the number, patterns or dimensions of the objects. The foreground of a video frame is the region of the frame that contains the object as well as its cast shadow. A process of object segmentation generally results in segmenting the entire foreground. Thus, shadow removal from the segmented foreground is essential for object segmentation. A novel computationally efficient shadow removal technique based on multiple features is proposed. Multiple object masks, each based on a single feature, are constructed and merged together to form a single object mask. The main idea of the proposed technique is that an object pixel is less likely to be indistinguishable from the shadow pixels simultaneously with respect to all the features used. Extensive simulations are performed by applying the proposed and some existing techniques to challenging video sequences for object segmentation and shadow removal. The subjective and objective results demonstrate the effectiveness and superiority of the schemes developed in this thesis

Banque de données et banc d'essai en détection de changement

Les caméras de vidéosurveillance sont de plus en plus présentes dans notre société, à un point tel que les séquences vidéo sont souvent enregistrées sans être regardées par des agents de sécurité. Il convient donc de créer des algorithmes qui vont effectuer le même travail d'analyse que des surveillants humains. Bien qu'il y ait des inquiétudes au niveau de la vie privée, on peut envisager maintes applications, toutes au service de la société. La détection de changement est à la base de bon nombre d'applications en analyse vidéo. Elle consiste à détecter tout changement intéressant dans une séquence capturée par une caméra fixe. Bien que les méthodes gèrent mieux les difficultés inhérentes à ce problème, il n'y a pas encore de solution définitive à la détection de changement. Avec des milliers de méthodes disponibles dans la littérature, il est présentement très difficile, voire impossible, de comparer ces méthodes et d'identifier lesquelles répondent mieux aux différents défis. Les auteurs font face au même problème lorsqu'ils désirent se comparer à l'état de l'art. Pour faire face à cette situation, nous avons créé un banc d'essai en détection de changement. Ceci inclut la création d'une banque de données d'envergure, d'une méthode d'évaluation quantitative équitable et d'un site web pour consulter le classement et télé-charger les résultats de segmentation des compétiteurs. Des outils et de la documentation pour utiliser ces derniers sont aussi offerts, le tout accessible gratuitement et simplement sur Internet. Comme le but est de devenir le standard de facto , le projet est suffisamment complet et intéressant pour convaincre la communauté scientifique de l'adopter. Ce faisant, nous avons créé notre propre programme d'annotation libre, rassemblé et programmé une dizaine de méthodes de détection de changement, puis déterminé les meilleures méthodes et les difficultés auxquelles la communauté devrait s'attaquer au cours des prochaines années. L'atelier organisé à CVPR 2012, le grand nombre de soumissions et le bon achalandage du site sont des indicateurs encourageants quant à la réussite de notre travail. Il est encore trop tôt pour confirmer quoi que ce soit car l'adoption d'un nouveau standard prend du temps, mais notre pronostic est positif

Deliverable D1.1 State of the art and requirements analysis for hypervideo

This deliverable presents a state-of-art and requirements analysis report for hypervideo authored as part of the WP1 of the LinkedTV project. Initially, we present some use-case (viewers) scenarios in the LinkedTV project and through the analysis of the distinctive needs and demands of each scenario we point out the technical requirements from a user-side perspective. Subsequently we study methods for the automatic and semi-automatic decomposition of the audiovisual content in order to effectively support the annotation process. Considering that the multimedia content comprises of different types of information, i.e., visual, textual and audio, we report various methods for the analysis of these three different streams. Finally we present various annotation tools which could integrate the developed analysis results so as to effectively support users (video producers) in the semi-automatic linking of hypervideo content, and based on them we report on the initial progress in building the LinkedTV annotation tool. For each one of the different classes of techniques being discussed in the deliverable we present the evaluation results from the application of one such method of the literature to a dataset well-suited to the needs of the LinkedTV project, and we indicate the future technical requirements that should be addressed in order to achieve higher levels of performance (e.g., in terms of accuracy and time-efficiency), as necessary