Novel statistical modeling methods for traffic video analysis

Video analysis is an active and rapidly expanding research area in computer vision and artificial intelligence due to its broad applications in modern society. Many methods have been proposed to analyze the videos, but many challenging factors remain untackled. In this dissertation, four statistical modeling methods are proposed to address some challenging traffic video analysis problems under adverse illumination and weather conditions. First, a new foreground detection method is presented to detect the foreground objects in videos. A novel Global Foreground Modeling (GFM) method, which estimates a global probability density function for the foreground and applies the Bayes decision rule for model selection, is proposed to model the foreground globally. A Local Background Modeling (LBM) method is applied by choosing the most significant Gaussian density in the Gaussian mixture model to model the background locally for each pixel. In addition, to mitigate the correlation effects of the Red, Green, and Blue (RGB) color space on the independence assumption among the color component images, some other color spaces are investigated for feature extraction. To further enhance the discriminatory power of the input feature vector, the horizontal and vertical Haar wavelet features and the temporal information are integrated into the color features to define a new 12-dimensional feature vector space. Finally, the Bayes classifier is applied for the classification of the foreground and the background pixels. Second, a novel moving cast shadow detection method is presented to detect and remove the cast shadows from the foreground. Specifically, a set of new chromatic criteria is presented to detect the candidate shadow pixels in the Hue, Saturation, and Value (HSV) color space. A new shadow region detection method is then proposed to cluster the candidate shadow pixels into shadow regions. A statistical shadow model, which uses a single Gaussian distribution to model the shadow class, is presented to classify shadow pixels. Additionally, an aggregated shadow detection strategy is presented to integrate the shadow detection results and remove the shadows from the foreground. Third, a novel statistical modeling method is presented to solve the automated road recognition problem for the Region of Interest (RoI) detection in traffic video analysis. A temporal feature guided statistical modeling method is proposed for road modeling. Additionally, a model pruning strategy is applied to estimate the road model. Then, a new road region detection method is presented to detect the road regions in the video. The method applies discriminant functions to classify each pixel in the estimated background image into a road class or a non-road class, respectively. The proposed method provides an intra-cognitive communication mode between the RoI selection and video analysis systems. Fourth, a novel anomalous driving detection method in videos, which can detect unsafe anomalous driving behaviors is introduced. A new Multiple Object Tracking (MOT) method is proposed to extract the velocities and trajectories of moving foreground objects in video. The new MOT method is a motion-based tracking method, which integrates the temporal and spatial features. Then, a novel Gaussian Local Velocity (GLV) modeling method is presented to model the normal moving behavior in traffic videos. The GLV model is built for every location in the video frame, and updated online. Finally, a discriminant function is proposed to detect anomalous driving behaviors. To assess the feasibility of the proposed statistical modeling methods, several popular public video datasets, as well as the real traffic videos from the New Jersey Department of Transportation (NJDOT) are applied. The experimental results show the effectiveness and feasibility of the proposed methods

Fusing Vantage Point Trees and Linear Discriminants for Fast Feature Classification

This paper describes a classification strategy that can be regarded as amore general form of nearest-neighbor classification. It fuses the concepts ofnearestneighbor,linear discriminantandVantage-Pointtrees, yielding an efficient indexingdata structure and classification algorithm. In the learning phase, we define a set ofdisjoint subspaces of reduced complexity that can be separated by linear discrimi-nants, ending up with an ensemble of simple (weak) classifiers that work locally. Inclassification, the closest centroids to the query determine the set of classifiers con-sidered, which responses are weighted. The algorithm was experimentally validatedin datasets widely used in the field, attaining error rates that are favorably compara-ble to the state-of-the-art classification techniques. Lastly, the proposed solution hasa set of interesting properties for a broad range of applications: 1) it is determinis-tic; 2) it classifies in time approximately logarithmic with respect to the size of thelearning set, being far more efficient than nearest neighbor classification in terms ofcomputational cost; and 3) it keeps the generalization ability of simple models.info:eu-repo/semantics/publishedVersio

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Face Centered Image Analysis Using Saliency and Deep Learning Based Techniques

Image analysis starts with the purpose of configuring vision machines that can perceive like human to intelligently infer general principles and sense the surrounding situations from imagery. This dissertation studies the face centered image analysis as the core problem in high level computer vision research and addresses the problem by tackling three challenging subjects: Are there anything interesting in the image? If there is, what is/are that/they? If there is a person presenting, who is he/she? What kind of expression he/she is performing? Can we know his/her age? Answering these problems results in the saliency-based object detection, deep learning structured objects categorization and recognition, human facial landmark detection and multitask biometrics. To implement object detection, a three-level saliency detection based on the self-similarity technique (SMAP) is firstly proposed in the work. The first level of SMAP accommodates statistical methods to generate proto-background patches, followed by the second level that implements local contrast computation based on image self-similarity characteristics. At last, the spatial color distribution constraint is considered to realize the saliency detection. The outcome of the algorithm is a full resolution image with highlighted saliency objects and well-defined edges. In object recognition, the Adaptive Deconvolution Network (ADN) is implemented to categorize the objects extracted from saliency detection. To improve the system performance, L1/2 norm regularized ADN has been proposed and tested in different applications. The results demonstrate the efficiency and significance of the new structure. To fully understand the facial biometrics related activity contained in the image, the low rank matrix decomposition is introduced to help locate the landmark points on the face images. The natural extension of this work is beneficial in human facial expression recognition and facial feature parsing research. To facilitate the understanding of the detected facial image, the automatic facial image analysis becomes essential. We present a novel deeply learnt tree-structured face representation to uniformly model the human face with different semantic meanings. We show that the proposed feature yields unified representation in multi-task facial biometrics and the multi-task learning framework is applicable to many other computer vision tasks

Satellite image segmentation using RVM and Fuzzy clustering

Image segmentation is common but still very challenging problem in the area of image processing but it has its application in many industries and medical field for example target tracking, object recognition and medical image processing. The task of image segmentation is to divide image into number of meaningful pieces on the basis of features of image such as color, texture. In this thesis some recently developed fuzzy clustering algorithms as well as supervised learning classifier Relevance Vector Machine has been used to get improved solution. First of all various fuzzy clustering algorithms such as FCM, DeFCM are used to produce different clustering solutions and then we improve each solution by again classifying remaining pixels of satellite image using Relevance Vector Machine (RVM classifier. Result of different supervised learning classifier such as Support Vector Machine (SVM), Relevance Vector Machine (RVM), K-nearest neighbors (KNN) has been compared on basis of error rate and time. One of the major drawback of any clustering algorithm is their input argument that is number of clusters in unlabelled data. In this thesis an attempt has been made to evaluate optimal number of clusters present in satellite image using DAVIES-BOULDIN Index

Understanding the summer roosting habitat selection of the greater mouse-tailed bat (Rhinopoma microphyllum) and the small mouse-tailed bat (Rhinopoma muscatellum) in Iran

Roost for bats, which are responsible for a wide range of vital ecological and economic services, is crucial. Their availability affects both the geographic occurrence and the diversity of bat communities. Hence, understanding how bats use roosts and variables that influence these patterns could contribute to the development of management plans to ensure their survival. In this study, species distribution modeling of two bat species, the greater mouse-tailed bat (Rhinopoma microphyllum) and the small mouse-tailed bat (Rhinopoma muscatellum), were carried out using the sdm package in R. To do so, 16 environmental variables were used as the predictors to explore their relationships with the occurrence of the two species using 12 modeling algorithms. The prediction models for each species were then combined into an ensemble model. The random forest modeling algorithm showed better performance than the other individual models in this modeling. Moreover, the prediction performance of the ensemble model was more substantial than all the individual models for both species. For the greater mouse-tailed bat, elevation, annual mean temperature, temperature seasonality, and distance to roads-railways were identified as the essential variables for summer roosting habitat selection. Meanwhile, distance to roads-railways, annual mean temperature, elevation, and distance to the ridge were significant for the small mouse-tailed bat. Since this study facilitates the management of future and suitable habitats by identifying important environmental conditions, it can be used in conservation plans

Curve Sign Inventorying Method Using Smartphones and Deep Learning Technologies

The objective of the proposed research is to develop and assess a system using smartphones and deep learning technologies to automatically establish an intelligent and sustainable curve sign inventory from videos. The Manual on the Uniform Traffic Control Devices (MUTCD) is the nationwide regulator that defines the standards used for transportation asset installation and maintenance. The proposed system is one of the components of a larger methodology whose purpose is to accomplish a frequent and cost-effective MUTCD curve sign compliance checking and other curve safety checking in order to reduce the number of deadly crashes on curves. To automatically build an effective sign inventory from videos, four modules are needed: sign detection, classification, tracking and localization. For this purpose, a pipeline has been developed in the past by former students of the Transportation laboratory of Georgia Tech. However, this pipeline is not accurate enough and its different modules have never been critically tested and assessed. Therefore, the objective of this study is to improve the different modules and particularly the detection module, which is the most important module of the pipeline, and to critically assess these improved modules to determine the pipeline ability to build an effective sign inventory. The proposed system has been tested and assessed in real conditions on a mountain road with many curves and curve signs; it has shown that the detection module is able to detect every single curve sign with a very low number of detected non-curve signs (false positive), resulting in a precision of 0.97 and a recall of 1. The other modules also showed very promising results. Overall, this study demonstrates that the proposed system is suitable for building an accurate curve sign inventory that can be used by transportation agencies to get a precise idea of the condition of the curve sign networks on a particular road.M.S