Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras

Feature Selection Using Genetic Algorithms for Human Gait Recognition

Many research studies have demonstrated that gait can serve as a useful biometric feature for human identification at a distance. Here we manifest the importance of feature selection in gait recognition systems. Feature selection is an important factor which impacts the classification accuracy. This goal is achieved by discarding irrelevant and redundant information which affects both the classifier's performance and system's efficiency. Traditional gait recognition systems have mostly been evaluated without considering the most relevant features. In this study, we are going to investigate the use of Genetic Algorithm (GA) for selecting an optimal subset of features for a model-free gait recognition approach without degrading the classification accuracy. First, features are extracted using Kernel Principal Component Analysis (KPCA) on four spatio-temporal projections of silhouettes. Then, GAs are applied to choose a subset of Eigen-vectors that represent a subject's identity. Our experimental results, conducted on Georgia Tech (GT) database, indicate considerable performance improvements

Novel system identification method and multi-objective-optimal multivariable disturbance observer for electric wheelchair

Electric wheelchair (EW) is subject to diverse types of terrains and slopes, but also to occupants of various weights, which causes the EW to suffer from highly perturbed dynamics. A precise multivariable dynamics of the EW is obtained using Lagrange equations of motion which models effects of slopes as output-additive disturbances. A static pre-compensator is analytically devised which considerably decouples the EW's dynamics and also brings about a more accurate identification of the EW. The controller is designed with a disturbance-observer (DOB) two-degree-of-freedom architecture, which reduces sensitivity to the model uncertainties while enhancing rejection of the disturbances. Upon disturbance rejection, noise reduction, and robust stability of the control system, three fitness functions are presented by which the DOB is tuned using a multi-objective optimization (MOO) approach namely non-dominated sorting genetic algorithm-II (NSGA-II). Finally, experimental results show desirable performance and robust stability of the proposed algorithm. (C) 2012 ISA. Published by Elsevier Ltd. All rights reserved