With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. As a sub-field, Interactive Image Processing emphasizes particularly on the communications between machine and human. The basic flowchart is definition of object, analysis and training phase, recognition and feedback. Generally speaking, the core issue is how we define the interesting object and track them more accurately in order to complete the interaction process successfully. This thesis proposes a novel dynamic simulation scheme for interactive image processing. The work consists of two main parts: Hand Motion Detection and Hand Gesture recognition. Within a hand motion detection processing, movement of hand will be identified and extracted. In a specific detection period, the current image is compared with the previous image in order to generate the difference between them. If the generated difference exceeds predefined threshold alarm, a typical hand motion movement is detected. Furthermore, in some particular situations, changes of hand gesture are also desired to be detected and classified. This task requires features extraction and feature comparison among each type of gestures. The essentials of hand gesture are including some low level features such as color, shape etc. Another important feature is orientation histogram. Each type of hand gestures has its particular representation in the domain of orientation histogram. Because Gaussian Mixture Model has great advantages to represent the object with essential feature elements and the Expectation-Maximization is the efficient procedure to compute the maximum likelihood between testing images and predefined standard sample of each different gesture, the comparability between testing image and samples of each type of gestures will be estimated by Expectation-Maximization algorithm in Gaussian Mixture Model. The performance of this approach in experiments shows the proposed method works well and accurately.

No description available.

No description available.

No description available.

Companies face a problem where employees reserve rooms and do not show up, which leadsto money and resources loss for the companies. An application capable of detecting thepresence of people in a room could solve this problem.This thesis details the process of building an Android application capable of detectingthe presence of people in a static room using motion detection. The application wasdeveloped through a five-staged process and evaluated by performing experiments whichmeasured the accuracy of the application.The finished application is installed on a Sony Xperia M4 Aqua device which is mountedhigh up on a wall in a conference room where the application takes images of the room. Theapplication is connected to a Google Drive account where the application uploads acquiredimages with an appropriate label. The application achieved an accuracy of 94.18% in anexperiment where 550 images where taken automatically by the application in differentconference rooms with and without people inside them

Övervakningssystem har haft en viktig roll i samhället under en lång period. Syftet med dessasystemen har inte ändrats, det är alltid säkerhetsaspekten som har varit huvudpunkten.Effektiviteten på ett övervakningssystem beror oftast på den mänskliga faktorn, någon måstebearbeta bildflödet som spelas in för att dra definitiva slutsatser. Under senare år har dessaövervakningssystem, i takt med teknologins utveckling, blivit mer intelligenta. Metoder somtillämpar automatisk analysering av en given bild har introducerats, vissa av dessa teknikerhar redan hunnit bli en standard. Denna studie ska utveckla en prototyp som använder sig avtekniken och stresstesta för att identifiera eventuella brister. Det är i grunden en feasibilitystudy som ska visa vad som kan uppnås med begränsade resurser. Med en funktionellprototyp tillgänglig skall diverse stresstester specifieras för att mäta systemets gränser.Resultatet har överlag varit positiva men ett antal kritiska brister identifierades. / Security systems have always had an important role in society for a long time. The purpose ofthese systems has always remained primarily focused on the security aspect. In most cases theefficiency of current security systems depend on human factors. The frames have to beprocessed by a person to reach a definitive conclusion. In recent years, security systems havebecome increasingly intelligent as technology continues to develop. Methods that implementautomatic analysis of any given frame in a video have been introduced and some of thesemethods are already standardized. The aim of this study is to develop a prototype and stresstest it to identify possible flaws. This study is primarily a feasibility study to show thepossibilities of what can be achieved with limited resources. With a fully functional prototypeavailable a series of stress tests will be specified to measure the system’s limits. The resultshave been positive overall but a number of critical flaws have been identified.

Due to the wide field of view, omnidirectional cameras have been extensively used in many applications, including surveillance and autonomous navigation. In order to implement a fully autonomous system, one of the essential problems is construction of an accurate, dynamic environment model. In Computer Vision this is called structure from stereo or motion (SFSM). The work in this dissertation addresses omnidirectional vision based SFSM for the navigation of an autonomous surface vehicle (ASV), and implements a vision system capable of locating stationary obstacles and detecting moving objects in real time. The environments where the ASV navigates are complex and fully of noise, system performance hence is a primary concern. In this dissertation, we thoroughly investigate the performance of range estimation for our omnidirectional vision system, regarding to different omnidirectional stereo configurations and considering kinds of noise, for instance, disturbances in calibration, stereo configuration, and image processing. The result of performance analysis is very important for our applications, which not only impacts the ASV's navigation, also guides the development of our omnidirectional stereo vision system. Another big challenge is to deal with noisy image data attained from riverine environments. In our vision system, a four-step image processing procedure is designed: feature detection, feature tracking, motion detection, and outlier rejection. The choice of point-wise features and outlier rejection based method makes motion detection and stationary obstacle detection efficient. Long run outdoor experiments are conducted in real time and show the effectiveness of the system. / Ph. D.

This report shows that a reliable motion detection is needed to make an accurate prediction of future activity. Several experiments are carried out to obtain information about the object ́s behaviour and the best settings for the motion detection. A moving object is captured using two cameras, for two image sequences, and motion detection is applied to the stereoscopic data. Background subtraction algorithm followed by image segmentation algorithm, morphology algorithm, and blob analy- sis are performed on the images to find the coordinates for the centroid of the moving object. Two models are created to make a statistical inter- pretation of the data: one model for the height over the width and one statistical model for the distance between the cameras and the moving object over the width. The mean and standard deviation values are calculated to make a reliable interpretation of the captured images and the moving object. The Kalman filter is used for the prediction of future activity. The filters of the statistical models are trained with the first coordinates of the detected balls, and the next coordinates are predicted.

Technological developments and innovations of the first forty years of the digital era have primarily addressed either the audio or the visual senses. Consequently, designers have primarily focused on the audio or the visual aspects of design. In the perspective of video surveillance, the data under consideration has always been visual. However, in light of the new behavioral and physiological studies which established a proof of cross modality in human perception i.e. humans do not process audio and visual stimulus separately, but percieve a scene based on all stimulus available, similar cues are being used to develop a surveillance system which uses both audio and visual data available. Human beings can easily associate a particular sound to an object in the surrounding. Drawing from such studies, we demonstrate a technique by which we can isolate concurrent audio and video events and associate them based on perceptual grouping principles. Associating sound to an object can form apart of larger surveillance system by producing a better description of objects. We represent audio in the pitch-time domain and use image processing algorithms such as line detection to isolate significant events. These events and are then grouped based on gestalt principles of proximity and similarity which operates in audio. Once auditory events are isolated we can extract their periodicity. In video, we can extract objects by using simple background subtraction. We extract motion and shape periodicities of all the objects by tracking their position or the number of pixels in each frame. By comparing all the periodicities in audio and video using a simple index we can easily associate audio to video. We show results on five scenariosin outdoor settings with different kinds of human activity such as running, walking and other moving objects such as balls and cars.

Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very dicult for a human op- erator to eectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identication at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the eective use of more advanced technolo- gies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identication. Before an object can be tracked, it must be detected. Motion segmentation tech- niques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erro- neous motion caused by noise and lighting eects, or due to the detection routines being unable to split occluded regions into their component objects. Particle l- ters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (of- ten manual) detection to initialise the lter. Particle lters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle lter. A novel hybrid motion segmentation / optical ow algorithm, capable of simulta- neously extracting multiple layers of foreground and optical ow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical ow is capable of extracting a mov- ing object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and signi- cant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle lter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benet from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle lter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking sys- tems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classication in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a signicant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi- automated video processing and therefore improve security in areas under surveil- lance.