Query compilation and hardware acceleration are important technologies for optimizing the performance of data processing engines. There have been many works on the exploration and adoption of these techniques in recent years. However, a number of engines still refrain from adopting them because of some reasons. One of the common reasons claims that the intricacies of these techniques make engines too complex to maintain. Another major barrier is the lack of widely accepted architectures and libraries of these techniques, which leads to the adoption often starting from scratch with lots of effort. In this paper, we propose Intel Big Data Analytic Toolkit (BDTK), an open-source C++ acceleration toolkit library for analytical data processing engines. BDTK provides lightweight, easy-to-connect, reusable components with interoperable interfaces to support query compilation and hardware accelerators. The query compilation in BDTK leverages vectorized execution and data-centric code generation to achieve high performance. BDTK could be integrated into different engines and helps them to adapt query compilation and hardware accelerators to optimize performance bottlenecks with less engineering effort.

The globalization of the economy and the increase in network bandwidth have contributed significantly to the development and popularity of real-time communication (RTC) social applications. RTC media streams, such as video meetings and calls, require more network resources and real-time performance than other services. In order to meet the requirements of RTC application providers to offer a higher level of service to their subscribers, Internet Service Providers (ISPs) need to identify the application to which the RTC media stream belongs. There are already some studies on traffic identification. However, the extant work is not yet able to distinguish the corresponding applications from the same type of media streams in real time. In addition, most of the work is not validated with actual data containing massive background traffic. Hence, we propose a real-time application identification method for meeting and calling RTC media streams in social networks. By analyzing the encrypted traffic, the method extracts features from the unit-time traffic aggregation without using payload and related information fields. The generated feature sequences are fed to our lightweight model. Our proposed method does not depend on initial packets or whole flows, and only an arbitrary 3-second traffic block is needed to achieve over 99% accuracy. Moreover, experiments using high-speed network traffic reflect that our approach can identify corresponding applications from RTC media streams in real time. Besides, comparisons with similar work show that this method requires only 1/160th of the memory and 1/10th of the processing time.

Visual tracking serves as a significant module in many applications. However, the heavy computation and low speed of many recent trackers restrict their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter based trackers limits their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter based trackers and propose an efficient and adaptive training sample update scheme. Training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with difference hashing algorithm(DHA) or discarded according to tracking result reliability. Experiments on OTB-2015, Temple Color 128 and UAV123 demonstrate our tracker performs favourably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2GHz).

The mean shift algorithm has been introduced successfully into the field of computer vision to be an efficient approach for visual tracking but the tracker has been awkward in handling the scale change of the object. This study addresses the scale estimation problem of the mean shift tracker, and proposes a novel method which is based on invariant foreground occupation ratio to solve this problem. The foreground occupation ratio is defined as the proportion of the foreground pixels in an image region. By taking an analysis of the foreground occupation ratio, the authors obtain its three simple properties. With its property of scale invariance, an iterative approximation approach is employed to estimate the scale of the foreground in the current image. The scale value is modified by a weighting function, and it is adjusted along the two axes with respect to the width and the height of the target. The scale estimation algorithm is then employed in the mean shift tracker to obtain the ability of scale adaptation for tracking. Experimental results show that, using the authors method for object scale estimation, the mean shift tracker performs well in tracking the target efficiently when its scale continuously changes.

Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or key point detectors.

This paper describes a novel hierarchy algorithm for extracting bridges over water in optical images. To reduce the omission of bridges by searching the edge, we extract the river regions which the bridges are included in. Firstly, we segment the optical image to get the coarse water bodies using iterative threshold, eliminate the noise regions and add the missing regions based on k-means clustering with texture information and spatial coherence. Then, the blanks are connected based on shape features and candidate bridge regions are segmented from river regions. Finally, the bridges are verified by geometric information and the ubiety between bridges and river. The results show that this approach is efficient and effective for extracting bridges in satellite image from Google Earth and in aerial optical images acquired by unmanned aerial vehicle.