Application of PREM Model to Parallel Implementation of KCF Tracker

V posledních letech se mnoho real-time embedded systémů vytváří za pomocí běžně komerčně dostupných (COTS) komponentů kvůli jejich ceně. Celkový výkon komponentů COTS je často mnohem vyšší než komponentů vytvořených speciálně pro real-time systémy. Komponenty COTS jsou však obvykle navrženy pro průměrný scénář a nedostatečná nebo žádná pozornost je věnována zárukám na čas v nejhorším případech vyžadovaných real-time systémy. V této práci realizujeme různé paralelní a rozšířené verze KCF trackeru jak pro CPU, tak i GPU a pokoušíme se otestovat prototyp HERCULES kompilátoru, který dovolí automaticky přeměnit části programu tak, aby odpovídaly PRedictable Execution Model (PREM), který by měl poskytovat silnější záruky na časování v nejhorším případě.In recent years many real-time embedded systems are being built using the Commercial-Off-The-Shelf (COTS) components because of their price. COTS component's overall performance is often much higher than specialized custom-made systems used in real-time systems. However, COTS components are typically designed for average case scenario, and little or no attention is put into worst-case timing guarantees required by real-time systems. In this thesis, we implement various parallel and extended versions of the KCF tracker for both CPU and GPU and try to test out the prototype HERCULES compiler, which allows converting automatically parts of the program to conform to PRedictable Execution Model (PREM), which should provide stronger worst-case timing guarantees

Vision based behavior recognition of laboratory animals for drug analysis and testing

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 92-101.In pharmacological experiments, a popular method to discover the effects of psychotherapeutic drugs is to monitor behaviors of laboratory mice subjected to drugs by vision sensors. Such surveillance operations are currently performed by human observers for practical reasons. Automating behavior analysis of laboratory mice by vision-based methods saves both time and human labor. In this study, we focus on automated action recognition of laboratory mice from short video clips in which only one action is performed. A two-stage hierarchical recognition method is designed to address the problem. In the first stage, still actions such as sleeping are separated from other action classes based on the amount of the motion area. Remaining action classes are discriminated by the second stage for which we propose four alternative methods. In the first method, we project 3D action volume onto 2D images by encoding temporal variations of each pixel using discrete wavelet transform (DWT). Resulting images are modeled and classified by hidden Markov models in maximum likelihood sense. The second method transforms action recognition problem into a sequence matching problem by explicitly describing pose of the subject in each frame. Instead of segmenting the subject from the background, we only take temporally active portions of the subject into consideration in pose description. Histograms of oriented gradients are employed to describe poses in frames. In the third method, actions are represented by a set of histograms of normalized spatio-temporal gradients computed from entire action volume at different temporal resolutions. The last method assumes that actions are collections of known spatio-temporal templates and can be described by histograms of those. To locate and describe such templates in actions, multi-scale 3D Harris corner detector and histogram of oriented gradients and optical flow vectors are employed, respectively. We test the proposed action recognition framework on a publicly available mice action dataset. In addition, we provide comparisons of each method with well-known studies in the literature. We find that the second and the fourth methods outperform both related studies and the other two methods in our framework in overall recognition rates. However, the more successful methods suffer from heavy computational cost. This study shows that representing actions as an ordered sequence of pose descriptors is quite effective in action recognition. In addition, success of the fourth method reveals that sparse spatio-temporal templates characterize the content of actions quite well.Sandıkcı, SelçukM.S

Fast Multi-scale fHOG Feature Extraction Using Histogram Downsampling

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