Autonomous Vision Based Facial and voice Recognition on the Unmanned Aerial Vehicle

The development of human navigation and tracking in the real time environment will lead to the implementation of more advanced tasks that can performed by the autonomous robots. That means, we proposed new intelligent algorithm for human identification using difficult of facial and speech which can substantially improve the rate of recognition as compared to the biometric identification for Robust system development. This project system that can recognize face using Eigenface recognizer with Principal component analysis (PCA) and human voice using the Hidden Markov Model(HMM) and. Also in this paper, combinations of algorithms such as modified Eigenface, Haar-Cascade classifier, PCA and HMM resulted in a more robust system for facial and speech recognition. The proposed system was implemented on AR drone 2.0 using the Microsoft Visual Studio 2015 platform together with EmguCV. The testing of the proposed system carried out in an indoor environment in order to evaluate its performance in terms of detection distance, angle of detection, and accuracy of detection. 500 images of different people were used for face recognition at detection distances. The best average result of 92.22% was obtained at a detection

Beyond the Camera: Neural Networks in World Coordinates

Eye movement and strategic placement of the visual field onto the retina, gives animals increased resolution of the scene and suppresses distracting information. This fundamental system has been missing from video understanding with deep networks, typically limited to 224 by 224 pixel content locked to the camera frame. We propose a simple idea, WorldFeatures, where each feature at every layer has a spatial transformation, and the feature map is only transformed as needed. We show that a network built with these WorldFeatures, can be used to model eye movements, such as saccades, fixation, and smooth pursuit, even in a batch setting on pre-recorded video. That is, the network can for example use all 224 by 224 pixels to look at a small detail one moment, and the whole scene the next. We show that typical building blocks, such as convolutions and pooling, can be adapted to support WorldFeatures using available tools. Experiments are presented on the Charades, Olympic Sports, and Caltech-UCSD Birds-200-2011 datasets, exploring action recognition, fine-grained recognition, and video stabilization

GlobalFlowNet: Video Stabilization using Deep Distilled Global Motion Estimates

Videos shot by laymen using hand-held cameras contain undesirable shaky motion. Estimating the global motion between successive frames, in a manner not influenced by moving objects, is central to many video stabilization techniques, but poses significant challenges. A large body of work uses 2D affine transformations or homography for the global motion. However, in this work, we introduce a more general representation scheme, which adapts any existing optical flow network to ignore the moving objects and obtain a spatially smooth approximation of the global motion between video frames. We achieve this by a knowledge distillation approach, where we first introduce a low pass filter module into the optical flow network to constrain the predicted optical flow to be spatially smooth. This becomes our student network, named as \textsc{GlobalFlowNet}. Then, using the original optical flow network as the teacher network, we train the student network using a robust loss function. Given a trained \textsc{GlobalFlowNet}, we stabilize videos using a two stage process. In the first stage, we correct the instability in affine parameters using a quadratic programming approach constrained by a user-specified cropping limit to control loss of field of view. In the second stage, we stabilize the video further by smoothing global motion parameters, expressed using a small number of discrete cosine transform coefficients. In extensive experiments on a variety of different videos, our technique outperforms state of the art techniques in terms of subjective quality and different quantitative measures of video stability. The source code is publicly available at \href{https://github.com/GlobalFlowNet/GlobalFlowNet}{https://github.com/GlobalFlowNet/GlobalFlowNet}Comment: Accepted in WACV 202

Digital Video Stabilization

Ph.DDOCTOR OF PHILOSOPH

Selectively De-animating and Stabilizing Videos

This thesis presents three systems for editing the motion of videos. First, selectively de-animating videos seeks to remove the large-scale motions of one or more objects so that other motions are easier to see. The user draws strokes to indicate the regions that should be immobilized, and our algorithm warps the video to remove large-scale motion in regions while leaving finer-scale, relative motions intact. We then use a graph-cut-based optimization to composite the warped video with still frames from the input video to remove unwanted background motion. Our technique enables applications such as clearer motion visualization, simpler creation of artistic cinemagraphs, and new ways to edit appearance and motion paths in video. Second, we design a fully automatic system to create portrait cinemagraphs by tracking facial features and de-animating the video with respect to the face and torso. We then generate compositing weights automatically to create the final cinemagraph portraits.Third, we present a user-assisted video stabilization algorithm that is able to stabilize challenging videos when state-of-the-art automatic algorithms fail to generate a satisfactory result. Our system introduces two new modes of interaction that allow the user to improve an unsatisfactory automatically stabilized video. First, we cluster tracks and visualize them on the warped video. The user ensures that appropriate tracks are selected by clicking on track clusters to include or exclude them to guide the stabilization. Second, the user can directly specify how regions in the output video should look by drawing quadrilaterals to select and deform parts of the frame. Our algorithm then computes a stabilized video using the user-selected tracks, while respecting the user-modified regions