Adapting the streaming video based on the estimated motion position

In real time video streaming, the frames must meet their timing constraints, typically specified as their deadlines. Wireless networks may suffer from bandwidth limitations. To reduce the data transmission over the wireless networks, we propose an adaption technique in the server side by extracting a part of the video frames that considered as a Region Of Interest (ROI), and drop the part outside the ROI from the frames that are between reference frames. The estimated position of the selection of the ROI is computed by using the Sum of Squared Differences (SSD) between consecutive frames. The reconstruction mechanism to the region outside the ROI is implemented in the mobile side by using linear interpolation between reference frames. We evaluate the proposed approach by using Mean Opinion Score (MOS) measurements. MOS are used to evaluate two scenarios with equivalent encoding size, where the users observe the first scenario with low bit rate for the original videos, while for the second scenario the users observe our proposed approach with high bit rate. The results show that our technique significantly reduces the amounts of data are streamed over wireless networks, while the reconstruction mechanism will provides acceptable video quality

Visual effects compositing demo reel

The main goal of this project is to create a visual effects compositing demo-reel and a professional website trough the participation on three short films: ‘Carne de Gaviota’, ‘Hope’ and ‘Nostalgia’. The final outcome has been more than 40 high technical and visual quality shots, following visual effects industry standard workflow, procedures and software

Software and hardware variation in Symbian camera system

During the past decade, multimedia features in mobile phones have become common. Even the low-end category mobile phones are equipped with camera in order to capture digital images and record videos. Mobile phones are giving tough competition to standalone camera devices by providing quality imaging experience to the consumers. In order to lead and compete with the pack of global mobile device manufacturers, Nokia has to differentiate its mobile device offerings across the wide price range addressing different market requirements. This necessitates them to use different types of cameras and flash hardware modules across their mobile phone range resulting in different camera system configurations. To support the range of mobile phones with a single software operating system platform, effective software variation is required. Some of the possibilities with mobile phone camera system configurations are devices equipped with one or two camera modules along with multiple or no flash HW, camera sensors with resolutions ranging from VGA to 41 megapixels, camera modules with autofocus or fixed focus lenses, flash modules based on Xenon or LED technology and the camera system controlled by either application processor or dedicated image signal processor. Symbian OS is the software platform capable of supporting various Nokia mobile devices with different hardware configurations. This is possible due to extensive software variation mechanisms that the Symbian OS supports. This thesis is an effort in describing various camera system configurations within the Nokia Symbian mobile phones and the software variation being used in supporting those

True Real Time Pose Independent Face Detection Using Color Information and Skin Region Segmentation

The process of detecting a face from a video in real time is essential in applications such as human surveillance, human computer-interaction, and for further face recognition research purposes. In this paper, the face detection algorithm is divided into four stages namely, Video Database Acquisition (VDA), Frame Sequence Extraction (FSE), Skin Region Detection (SRD), and K-Mean Face Segmentation (KFS). Initially, the videos in MPEG format are converted to JPEG images depending on the user specified frame rate (FSE phase). During this conversion, the face detection process comprising of SRD and KFS phases runs on each of the images that are converted. The skin regions are detected in the images, which act as the input for the K-Mean Face Segmentation phase. The skin region clusters thus obtained are classified as face clusters depending on a threshold value. This algorithm was tested on 18 videos, which were acquired by the SONY DCR TRV-80 camera in the VDA phase, regardless of age, gender, size, race, and skin tones. Furthermore, the varying illumination conditions such as bright sunlight, sufficient light, and dim light conditions, and different orientations of the individuals in the videos were gracefully handled by the system. The time taken to detect and store the normalized faces was comparable to the length of the video and in some cases it was even less. Thus, this system works in True Real Time (TRT)

Video Stabilization, Camera Motion Pattern Recognition and Motion Tracking Using Spatiotemporal Regularity Flow

Abstract-In this paper we propose a different approach based on a spatio-temporal feature called the Spatio Temporal Regularity Flow (SPREF) to stabilize unwanted camera motions in a video, recognize the camera motion patterns between consecutive frames and Group of Frames(GOF) and track the motion of an object in a video with the background subtracted. The method for stabilization based on Camera Motion uses the Translational Regularity flow vectors (TSPREF). In this method we fit the TSPREF vectors into parametric model to calculate the unstabilized global motion. An adaptive Gaussian smoothing method is used to smoothen the global motion followed by motion compensation to produce a stabilized sequence. Experimental results are provided and the stabilization achieved is validated using the qualitative measure Interframe Transform Fidelity (ITF).In camera motion pattern recognition we make use of TSPREF vectors to recognize the cognizant camera motion patterns. This is done for consecutive frames as well as Group of Frames(GOF)of different video sequences. In motion tracking we use the TSPREF vectors to track the moving object present in a video. The test videos taken have a still background with one or two moving objects. In all the cases we have the background subtracted from the moving object

A utilização do vídeo para a divulgação de atividades de I&D&I

Tese de Mestrado. Multimédia. Faculdade de Engenharia. Universidade do Porto. 201

Vision Based Localization for Multiple UAVs and Mobile Robots

Master'sMASTER OF ENGINEERIN