Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition

Recently, several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches.Comment: doctoral dissertatio

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

Object Re-Identification Based on Deep Learning

With the explosive growth of video data and the rapid development of computer vision technology, more and more relevant technologies are applied in our real life, one of which is object re-identification (Re-ID) technology. Object Re-ID is currently concentrated in the field of person Re-ID and vehicle Re-ID, which is mainly used to realize the cross-vision tracking of person/vehicle and trajectory prediction. This chapter combines theory and practice to explain why the deep network can re-identify the object. To introduce the main technical route of object Re-ID, the examples of person/vehicle Re-ID are given, and the improvement points of existing object Re-ID research are described separately

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Learning Visual Patterns: Imposing Order on Objects, Trajectories and Networks

Fundamental to many tasks in the field of computer vision, this work considers the understanding of observed visual patterns in static images and dynamic scenes . Within this broad domain, we focus on three particular subtasks, contributing novel solutions to: (a) the subordinate categorization of objects (avian species specifically), (b) the analysis of multi-agent interactions using the agent trajectories, and (c) the estimation of camera network topology. In contrast to object recognition, where the presence or absence of certain parts is generally indicative of basic-level category, the problem of subordinate categorization rests on the ability to establish salient distinctions amongst the characteristics of those parts which comprise the basic-level category. Focusing on an avian domain due to the fine-grained structure of the category taxonomy, we explore a pose-normalized appearance model based on a volumetric poselet scheme. The variation in shape and appearance properties of these parts across a taxonomy provides the cues needed for subordinate categorization. Our model associates the underlying image pattern parameters used for detection with corresponding volumetric part location, scale and orientation parameters. These parameters implicitly define a mapping from the image pixels into a pose-normalized appearance space, removing view and pose dependencies, facilitating fine-grained categorization with relatively few training examples. We next examine the problem of leveraging trajectories to understand interactions in dynamic multi-agent environments. We focus on perceptual tasks, those for which an agent's behavior is governed largely by the individuals and objects around them. We introduce kinetic accessibility, a model for evaluating the perceived, and thus anticipated, movements of other agents. This new model is then applied to the analysis of basketball footage. The kinetic accessibility measures are coupled with low-level visual cues and domain-specific knowledge for determining which player has possession of the ball and for recognizing events such as passes, shots and turnovers. Finally, we present two differing approaches for estimating camera network topology. The first technique seeks to partition a set of observations made in the camera network into individual object trajectories. As exhaustive consideration of the partition space is intractable, partitions are considered incrementally, adding observations while pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model, balancing the consistency of appearances across a hypothesized trajectory with the latest predictions of camera adjacency. A primarily benefit of estimating object trajectories is that higher-order statistics, as opposed to just first-order adjacency, can be derived, yielding resilience to camera failure and the potential for improved tracking performance between cameras. Unlike the former centralized technique, the latter takes a decentralized approach, estimating the global network topology with local computations using sequential Bayesian estimation on a modified multinomial distribution. Key to this method is an information-theoretic appearance model for observation weighting. The inherently distributed nature of the approach allows the simultaneous utilization of all sensors as processing agents in collectively recovering the network topology

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application