High Performance Video Stream Analytics System for Object Detection and Classification

Due to the recent advances in cameras, cell phones and camcorders, particularly the resolution at which they can record an image/video, large amounts of data are generated daily. This video data is often so large that manually inspecting it for object detection and classification can be time consuming and error prone, thereby it requires automated analysis to extract useful information and meta-data. The automated analysis from video streams also comes with numerous challenges such as blur content and variation in illumination conditions and poses. We investigate an automated video analytics system in this thesis which takes into account the characteristics from both shallow and deep learning domains. We propose fusion of features from spatial frequency domain to perform highly accurate blur and illumination invariant object classification using deep learning networks. We also propose the tuning of hyper-parameters associated with the deep learning network through a mathematical model. The mathematical model used to support hyper-parameter tuning improved the performance of the proposed system during training. The outcomes of various hyper-parameters on system's performance are compared. The parameters that contribute towards the most optimal performance are selected for the video object classification. The proposed video analytics system has been demonstrated to process a large number of video streams and the underlying infrastructure is able to scale based on the number and size of the video stream(s) being processed. The extensive experimentation on publicly available image and video datasets reveal that the proposed system is significantly more accurate and scalable and can be used as a general purpose video analytics system.N/

Directional EMD and Its Application to Texture Segmentation

Abstract In this paper we present the definition and framework of Directional Empirical Mode Decomposition (DEMD) and use DEMD to do texture segmentation. As a new technique of time-frequency analysis, EMD decomposes signals by sifting and then analyzes the instantaneous frequency of the obtained components called Intrinsic Mode Functions (IMFs). Compared with Bidimensional EMD (BEMD) which only extracts textures by radial basis function interpolation, the virtues of DEMD include: the directional quality is considered in this framework; four features can be extracted for each point from the decomposition. The technique of selecting directions for DEMD based on texture's Wold theory is also presented. Experimental results indicate the effectiveness of the method for texture segmentation. In addition, we show the explanation for the DEMD’s ability for texture classification from visual views