Cyclist Detection, Tracking, and Trajectory Analysis in Urban Traffic Video Data

The major objective of this thesis work is examining computer vision and machine learning detection methods, tracking algorithms and trajectory analysis for cyclists in traffic video data and developing an efficient system for cyclist counting. Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, a vision-based method for gathering cyclist count data at intersections and road segments is developed. First, we develop methodology for an efficient detection and tracking of cyclists. The combination of classification features along with motion based properties are evaluated to detect cyclists in the test video data. A Convolutional Neural Network (CNN) based detector called You Only Look Once (YOLO) is implemented to increase the detection accuracy. In the next step, the detection results are fed into a tracker which is implemented based on the Kernelized Correlation Filters (KCF) which in cooperation with the bipartite graph matching algorithm allows to track multiple cyclists, concurrently. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The trajectory comparison is performed based on semantic similarity approach. The proposed counting method is the first cyclist counting method that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis

Sparse-View CT Reconstruction Based on Nonconvex L1 − L2 Regularizations

The reconstruction from sparse-view projections is one of important problems in computed tomography (CT) limited by the availability or feasibility of obtaining of a large number of projections. Traditionally, convex regularizers have been exploited to improve the reconstruction quality in sparse-view CT, and the convex constraint in those problems leads to an easy optimization process. However, convex regularizers often result in a biased approximation and inaccurate reconstruction in CT problems. Here, we present a nonconvex, Lipschitz continuous and non-smooth regularization model. The CT reconstruction is formulated as a nonconvex constrained L1 − L2 minimization problem and solved through a difference of convex algorithm and alternating direction of multiplier method which generates a better result than L0 or L1 regularizers in the CT reconstruction. We compare our method with previously reported high performance methods which use convex regularizers such as TV, wavelet, curvelet, and curvelet+TV (CTV) on the test phantom images. The results show that there are benefits in using the nonconvex regularizer in the sparse-view CT reconstruction

Deep Learning for Multi-Channel Image Analysis with Applications to Remote Sensing and Biomedicine

Deep neural networks are emerging as a popular choice for multi-channel image analysis – compared with other machine learning approaches, they have been shown to be more effective for a variety of applications in hyperspectral (HSI) and multispectral imaging. We focus on application specific nuances and design choices with respect to deploying such networks for robust analysis of hyperspectral and multispectral images. We provide quantitative and qualitative results with a variety of deep learning architectures in remote sensing, biomedical FTIR and multiplex rat brain images. In this work, not only are traditional deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Convolutional-Recurrent Neural Networks (CRNNs) investigated, we also design and develop Graph based convolutional neural networks (GCNs) with applications to hyperspectral images. To optimize GCNs for HSI data, we proposed a new method of adjacency matrix construction (a semi-supervised adjacency matrix) which leverages class specific and cluster specific properties of the underlying imagery data. Finally, we designed a semi-automatic pipeline that utilizes registration and deep semantic segmentation for aligning (fitting) a brain atlas on multiplex rat brain images