Recurrent motion in vision

In this thesis, Recurrent Motion in Vision, we explore the concept of recurrent motion patterns in video. The thesis, consisting of two parts, begins with a study on the origin of periodic motion which results in its categorization into fundamental cases. The chapters that follow introduce novel solutions for estimating repetition in real-world videos by means of counting. In the second part, we investigate recurrent motion in physical scenes for learning intrinsic object properties

High-Speed Object Detection: Design, Study and Implementation of a Detection Framework using Channel Features and Boosting

In this thesis we design, implement and study a high-speed object detection framework. Our baseline detector uses integral channel features as object representation and AdaBoost as supervised learning algorithm. We suggest the implementation of two approximation techniques for speeding up the baseline detector and show their effectiveness by performing experiments on both detection quality and speed. The first improvement to our baseline classifier focuses on speeding up the classification of subwindows by formulating the problem as sequential decision process. The second improvement provides better multiscale handling to detect objects of all sizes without rescaling the input image. This speed-up builds upon the scale invariance property of image statistics in natural images that offers a powerful relationship for approximating feature responses of adjacent scales. While these techniques are not new itself, to our best knowledge we are the first to combine these into a framework for high-speed object detection. Our detection framework is built from the ground up using a fast GPU implementation. Based on these approximation techniques and the GPU implementation for extracting channel features we report detection speeds of 55 fps on a laptop. In a series of experiments we study the contribution of each component to the overall detection time and the possible change in detection quality due to the approximations. We train and test the detector on our car dataset that was constructed for this work. More specifically we focus on rear-view car detection. However the methods discussed are not limited to this object class.Pattern Recognition and BioinformaticsIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Real-World Repetition Estimation by Div, Grad and Curl

We consider the problem of estimating repetition in video, such as performing push-ups, cutting a melon or playing violin. Existing work shows good results under the assumption of static and stationary periodicity. As realistic video is rarely perfectly static and stationary, the often preferred Fourier-based measurements is inapt. Instead, we adopt the wavelet transform to better handle non-static and non-stationary video dynamics. From the flow field and its differentials, we derive three fundamental motion types and three motion continuities of intrinsic periodicity in 3D. On top of this, the 2D perception of 3D periodicity considers two extreme viewpoints. What follows are 18 fundamental cases of recurrent perception in 2D. In practice, to deal with the variety of repetitive appearance, our theory implies measuring time-varying flow F t and its differentials ΔF t , Δ·F t and Δ×F t over segmented foreground motion. For experiments, we introduce the new QUVA Repetition dataset, reflecting reality by including non-static and non-stationary videos. On the task of counting repetitions in video, we obtain favorable results compared to a deep learning alternative