Video Surveillance Analysis as a Context for Embedded Systems and Artificial Intelligence Education

Video surveillance analysis is an exciting, active research area and an important industry application. It is a multidisciplinary field that draws on signal processing, embedded systems, and artificial intelligence topics, and is well suited to motivate student engagement in all of these areas. This paper describes the benefits of the convergence of these topics, presents a versatile video surveillance analysis process that can be used as the basis for many investigations, and presents two template exercises in tracking detected targets and in evaluating runtime efficiency. The processing chain consists of detecting changes in a scene and locating and characterizing the resulting targets. The analysis is illustrated for targets in outdoor scenes using a variety of classification features. Also, sample code for processing is included

Vehicle Tracking and Classification via 3D Geometries for Intelligent Transportation Systems

In this dissertation, we present generalized techniques which allow for the tracking and classification of vehicles by tracking various Point(s) of Interest (PoI) on a vehicle. Tracking the various PoI allows for the composition of those points into 3D geometries which are unique to a given vehicle type. We demonstrate this technique using passive, simulated image based sensor measurements and three separate inertial track formulations. We demonstrate the capability to classify the 3D geometries in multiple transform domains (PCA & LDA) using Minimum Euclidean Distance, Maximum Likelihood and Artificial Neural Networks. Additionally, we demonstrate the ability to fuse separate classifiers from multiple domains via Bayesian Networks to achieve ensemble classification