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

Object Tracking in Video with Part-Based Tracking by Feature Sampling

Visual tracking of arbitrary objects is an active research topic in computer vision, with applications across multiple disciplines including video surveillance, activity analysis, robot vision, and human computer interface. Despite great progress having been made in object tracking in recent years, it still remains a challenge to design trackers that can deal with difficult tracking scenarios, such as camera motion, object motion change, occlusion, illumination changes, and object deformation. A promising way of tackling these types of problems is to use a part-based method; one which models and tracks small regions of the object and estimates the location of the object based on the tracked part's positions. These approaches typically model parts of objects with histograms of various hand-crafted features extracted from the region in which the part is located. However, it is unclear how such relatively homogeneous regions should be represented to form an effective part-based tracker. In this thesis we present a part-based tracker that includes a model for object parts that is designed to empirically characterise the underlying colour distribution of an image region, representing it by pairs of randomly selected colour features and counts of how many pixels are similar to each feature. This novel feature representation is used to find probable locations for the part in future frames via a Bhattacharyya Distance-based metric, which is modified to prefer higher quality matches. Sets of candidate patch locations are generated by randomly generating non-shearing affine transformations of the part's previous locations and locally optimising the most likely sets of parts to allow for small intra-frame object deformations. We also present a study of model initialisation in online, model-free tracking and evaluate several techniques for selecting the regions of an image, given a target bounding box most likely to contain an object. The strengths and limitations of the combined tracker are evaluated on the VOT2016 and VOT2018 datasets using their evaluation protocol, which also allows an extensive evaluation of parameter robustness. The presented tracker is ranked first among part-based trackers on the VOT2018 dataset and is particularly robust to changes in object and camera motion, as well as object size changes

Understanding the variability in vehicle dynamics and emissions at urban obstacles

Roadworks are a feature of the road network that can cause vehicles to deviate from their desired speed or trajectory. This may negatively impact traditional measures of network performance such as travel time, or result in changes to tailpipe emission rates. The impact of roadworks on tailpipe emission rates is of interest due to the harmful pollutants that are released during the combustion process. Pollutants such as nitrogen oxides (NOx) are toxic to humans, and carbon dioxide (CO2) is a greenhouse believed to influence human-induced global climate change. In order to investigate methods of reducing the environmental impact of roadworks and other obstacles in the road network, modelling tools may be used. However, it is essential that the tools are appropriate for modelling these features of the road network. In order to assess the suitability of existing traffic and emission modelling tools, an understanding of the variability in vehicle dynamics and emissions at urban obstacles is first required. In this thesis, a dataset that contains real-world tailpipe emissions and vehicle dynamics data, from vehicles in the vicinity of urban obstacles such as roadworks, is assembled. This is achieved using a portable emission measurement system (PEMS) and a high-resolution trajectory monitoring platform developed as part of this research. Through analysis of the acceleration behaviour and tailpipe emission rates at different urban obstacles and from different vehicles, an understanding of the variability is formed. The findings from the analysis of behaviours observed in the vicinity of urban obstacles are then used to adapt existing traffic and emissions modelling tools. The error between measured and modelled emissions is shown to reduce from over 30% to under 12% for CO2 emissions. Based on the findings of a roadworks case study, recommendations are made to policy makers and the modelling community.Open Acces