On the design and implementation of a high definition multi-view intelligent video surveillance system

This paper proposes a distributed architecture for high definition (HD) multi-view video surveillance system. It adopts a modular design where multiple intelligent Internet Protocol (IP)-based video surveillance cameras are connected to a local video server. Each server is equipped with storage and optional graphics processing units (GPUs) for supporting high-level video analytics and processing algorithms such as real-time decoding and tracking for the video captured. The servers are connected to the IP network for supporting distributed processing and remote data access. The DSP-based surveillance camera is equipped with realtime algorithms for streaming compressed videos to the server and performing simple video analytics functions. We also developed video analytics algorithms for security monitoring. Both publicly available data set and real video data that are captured under indoor and outdoor scenarios are used to validate our algorithms. Experimental results show that our distributed system can support real-time video applications with high definition resolution.published_or_final_versio

Real-time data acquisition, transmission and archival framework

Most human actions are a direct response to stimuli from their five senses. In the past few decades there has been a growing interest in capturing and storing the information that is obtained from the senses using analog and digital sensors. By storing this data it is possible to further analyze and better understand human perception. While many devices have been created for capturing and storing data, existing software and hardware architectures are aimed towards specialized devices and require expensive high-performance systems. This thesis aims to create a framework that supports capture and monitoring of a variety of sensors and can be scaled to run on low and high-performance systems such as netbooks, laptops and desktop systems. The proposed architecture was tested using aural and visual sensors due to their availability and higher bandwidth requirements compared to other sensors. Four different portable computing devices were used for testing with a varied set of hardware capabilities. On each of the systems the same suite of tests were run to benchmark and analyze CPU, memory, network, and storage usage statistics. From the results it was shown that on all of these platforms capturing data from multiple video, audio and other sensor sources was possible in real-time. Performance was shown to scale based on several factors, but the most important were CPU architecture, network topology and data interfaces used

TiFEE : an input event-handling framework with touchless device support

Tese de mestrado integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 201

DirectShow Approach to Low-Cost Multimedia Security Surveillance System

In response to the recent intensive needs for civilian security surveillance, both full and compact versions of a Multimedia Security Surveillance (MSS) system have been built up. The new Microsoft DirectShow technology was applied in implementing the multimedia stream-processing module. Through Microsoft Windows Driver Model interface, the chosen IEEE1394 enabled Fire-i cameras as external sensors are integrated with PC based continuous storage unit. The MSS application also allows multimedia broadcasting and remote controls. Cost analysis is included

Implementation of multi-algorithm controllers for path determination in mobile robot systems

textRecent advancements in control systems, such as the ones used in missile technology in the military or autonomous vehicle development have motivated this study in an attempt to explore various control algorithms and their implementation relevant those applications. Both missile interceptor and autonomous vehicle technology require precise and responsive control system to accurately determine the projectile path of pursuer to strike a moving target or reach a static finish line.The objective of this study is to investigate the performance of several control techniques for a mobile robot to autonomously track and pursue a moving object. Computer model is developed to numerically predict the path taken by the pursuer as it tracks an object moving in regular or random manner. In the computer simulation, the robot's path is calculated using three different techniques: reactive controller, linear estimation, and artificial neural network. Fitness of each method may be determined by evaluating the controller against several factors, such as interception time, steady-state positional error, steady-state time (settling time) and algorithm complexity, listed in decreasing order of importance. A working experimental model is developed to validate the controller selection determined from the computer model simulation. In the experimental setting, the primary inputs to the robot are visual images from cameras. The experiments are carried out with the robot receiving visual inputs from two different perspectives, overhead and frontal vision. Robust image processing technique becomes a topic of significant importance for the system. To manipulate visual images in real-time from raw inputs to comprehensible data, while maintaining fast computational time is a challenge that is addressed in this study. The results from computer simulations show that artificial neural network is a more powerful control algorithm, capable of estimating the object's path more accurately than the other two controllers, resulting in smaller steady-state positional error. The experimental results confirm this conclusion as artificial neural network outperforms the reactive and linear controller by intercepting the object more quickly, i.e. shorter interception time.Mechanical Engineerin