Feature-based tracking of multiple people for intelligent video surveillance.

Intelligent video surveillance is the process of performing surveillance task automatically by a computer vision system. It involves detecting and tracking people in the video sequence and understanding their behavior. This thesis addresses the problem of detecting and tracking multiple moving people with unknown background. We have proposed a feature-based framework for tracking, which requires feature extraction and feature matching. We have considered color, size, blob bounding box and motion information as features of people. In our feature-based tracking system, we have proposed to use Pearson correlation coefficient for matching feature-vector with temporal templates. The occlusion problem has been solved by histogram backprojection. Our tracking system is fast and free from assumptions about human structure. We have implemented our tracking system using Visual C++ and OpenCV and tested on real-world images and videos. Experimental results suggest that our tracking system achieved good accuracy and can process videos in 10-15 fps.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .A42. Source: Masters Abstracts International, Volume: 45-01, page: 0347. Thesis (M.Sc.)--University of Windsor (Canada), 2006

Timing layers, 4- and 5-dimension tracking

The combination of precision space and time information in particle tracking, the so-called 4D tracking, is being considered in the upgrade of the ATLAS, CMS, and LHCb experiments at the High-Luminosity LHC, set to start data taking in 2024–2025. Regardless of the type of solution chosen, space-time tracking brings benefits to the performance of the detectors by reducing the background and sharpening the resolution; it improves tracking performances and simplifies tracks combinatorics. Space-time tracking also allows investigating new physics channels, for example, it opens up the possibilities of new searches in long-living particles by measuring accurately the time of flight between the production and the decay vertexes. The foreseen applications of 4D tracking in experiments with very high acquisition rates, for example at HL-LHC, add one more dimension to the problem, increasing dramatically the complexity of the read-out system and that of the whole detector design: we call 5D tracking the application of 4D tracking in high rate environments

Detecting Motifs in System Call Sequences

The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify unknown motifs which repeat within time series data. The power of the algorithm is derived from its use of a small number of parameters with minimal assumptions. The algorithm searches from a completely neutral perspective that is independent of the data being analysed and the underlying motifs. In this paper the motif tracking algorithm is applied to the search for patterns within sequences of low level system calls between the Linux kernel and the operating system’s user space. The MTA is able to compress data found in large system call data sets to a limited number of motifs which summarise that data. The motifs provide a resource from which a profile of executed processes can be built. The potential for these profiles and new implications for security research are highlighted. A higher level system call language for measuring similarity between patterns of such calls is also suggested

XL-NBT: A Cross-lingual Neural Belief Tracking Framework

Task-oriented dialog systems are becoming pervasive, and many companies heavily rely on them to complement human agents for customer service in call centers. With globalization, the need for providing cross-lingual customer support becomes more urgent than ever. However, cross-lingual support poses great challenges---it requires a large amount of additional annotated data from native speakers. In order to bypass the expensive human annotation and achieve the first step towards the ultimate goal of building a universal dialog system, we set out to build a cross-lingual state tracking framework. Specifically, we assume that there exists a source language with dialog belief tracking annotations while the target languages have no annotated dialog data of any form. Then, we pre-train a state tracker for the source language as a teacher, which is able to exploit easy-to-access parallel data. We then distill and transfer its own knowledge to the student state tracker in target languages. We specifically discuss two types of common parallel resources: bilingual corpus and bilingual dictionary, and design different transfer learning strategies accordingly. Experimentally, we successfully use English state tracker as the teacher to transfer its knowledge to both Italian and German trackers and achieve promising results.Comment: 13 pages, 5 figures, 3 tables, accepted to EMNLP 2018 conferenc

Reference tracking mechanisms and automatic annotation based on Ob-Ugric information structure

The following paper is concerned with information structure in the Ob-Ugric languages and its manifestation in reference tracking and its mechanisms. We will show how both knowledge on information structure and on reference tracking mechanisms can be used to develop a system for a (semi-)automatic annotation of syntactic, semantic and pragmatic functions. We assume that the principles of information structure, i.e., the balancing of the content of an utterance, are indicated by the use of anaphoric devices to mark participants in an on-going discourse. This process in which participants are encoded by the speaker and decoded by the hearer is called reference tracking. Our model distinguishes four important factors that play a role in reference tracking: inherent (linguistic) features of a referent, information structure, referential devices and referential strategies. The interaction between these factors we call reference tracking mechanisms. Here, the passive voice and the dative shift are used to exemplify this complex interaction system. Drawing conclusions from this, rules are developed to annotate both syntactic, semantic and pragmatic roles of discourse participants (semi-)automatically

Acoustic motion tracking and its application

Video games, Virtual Reality (VR), Augmented Reality (AR), and Smart appliances (e.g., smart TVs) all call for a new way for users to interact and control them. This thesis explores high precision acoustic motion tracking system which aims to replace traditional control devices such as mouse and let the user play games, interact with VR/AR headsets, and control smart appliances. We develop a lightweight system which can achieve mm-level tracking accuracy using inaudible sounds. At the heart of our system lies a distributed Frequency Modulated Continuous Waveform (FMCW) which is able to accurately estimate the absolute distance between a receiver and a transmitter that are separate and unsynchronized. We further develop an optimization framework to combine FMCW estimation with Doppler shifts and Inertial Measurement Unit (IMU) measurements to enhance the accuracy, and efficient algorithm to solve the optimization problem. We develop several interesting applications on top of our motion tracking technology, including audio ruler, drawing in the air, and playing motion-controlled gamesComputer Science