With more than half of the world’s population living in urban areas, a huge in-town travel
demand is resulted and forecasted to continuously increase in next few decades. Particularly,
walking is a natural mode of mobility, which brings substantial socioeconomic benefits to a
society. Hence, creating a pleasant walking environment is essential to encourage citizens to
walk. Governments in different regions worldwide have been actively setting out visions to
incorporate urban walkability into new area development and urban renewal. Therefore,
systematic methodologies for walkability monitoring are highly demanded. Existing practices
mostly rely on statutory design guidelines, walking facility audits and pedestrian surveys.
However, these approaches tend to be time-consuming and labor-intensive, mainly due to the
complexity and regional uniqueness of walkability. Therefore, more effective methodologies
are needed for walkability monitoring. Nowadays, closed-circuit television (CCTV) cameras
are commonly adopted, which provide rich visual information about pedestrian appearance and
movement. Conventional computer vision techniques have been applied to automatically
process CCTV videos for pedestrian flow analytics. Yet, they require strong prior knowledge
of specific camera scenes to design hand-crafted feature extraction mechanism, which appears
very tedious and not scalable to different cameras. In recent studies, deep learning techniques
have been incorporated, which outperform conventional computer vision methods in several
image processing tasks. Deep neural networks are highly flexible in representing abundant and
complicated image patterns, whose feature extraction mechanism is automatically optimized
via a data-driven approach, i.e. directly feeding input images and labeled output, without
tediously hand-crafting intermediate features. Hence, deep learning techniques demonstrate
competitive performance for video processing. Therefore, this research aims to incorporate
deep learning-based computer vision techniques into CCTV analytics for automatically
analyzing pedestrian walking behavior, in order to facilitate pedestrian walkability monitoring.
Overall, this thesis consists of three parts. Part 1 is the literature review on existing
approaches for walkability monitoring and CCTV video analytics. Among previous studies,
four research gaps among existing methods are to be addressed: 1) limited robustness for
pedestrian tracking due to challenging scene conditions like occlusion, 2) limited in small areal
coverage and cannot analyze pedestrian walking behavior across different areas, 3) not scalable
when adapting deep learning models to different scenes requiring ineffective collection of
massive training data, and 4) lack of a comprehensive framework to capture infrastructural and
pedestrian-related attributes for quantitative walkability analyses. Therefore, Part 2 focuses on extracting pedestrian movement based on automated CCTV analytics, including three works:
1) pedestrian trajectory tracking within single camera, 2) pedestrian re-identification across
multiple cameras, and 3) transfer learning for scene-adaptive model adaptation. Subsequently,
Part 3 focuses on the development of an integrated framework for walkability analyses
integrating infrastructural characteristics and pedestrian behavior with CCTV analytics. More
specific elaboration of each research work is as follows.
Work 2.1: Pedestrian trajectory tracking within single camera. Existing methods of
pedestrian tracking suffer from several challenging conditions, such as inter-person occlusion
and appearance variations, which leads to ambiguous identities and hence inaccurate pedestrian
flow statistics. Therefore, a more robust methodology of pedestrian tracking is proposed which
1) incorporates high-level pedestrian attributes into enhancing pedestrian tracking, 2) presents
a similarity measure integrating multiple cues for identity matching, and 3) includes a probation
mechanism for more robust identity matching.
Work 2.2: Pedestrian re-identification across multiple cameras. Existing studies of
pedestrian flow analytics are limited in a small areal scale, while tracking the movement of a
person may require accurate identity matching across different cameras, which is challenging
due to the appearance variation and ambiguity among different persons. Therefore, a more
robust methodology of pedestrian re-identification is developed which 1) presents a new
convolutional neural network architecture that extracts discriminative-and-distributed human
features, 2) presents a generic approach of explainable model design by intuitively visualizing
feature extraction mechanism of deep learning models, and 3) includes an incremental feature
aggregation strategy designed for more robust identity matching.
Work 2.3: Scene-adaptive model deployment with transfer learning. Existing deep
learning models require massive amount of scene-specific training data, which hinders their
scalability of being deployed to practical multi-camera analytical system. Moreover, further
optimization of existing training strategies of pedestrian re-identification models is needed, to
facilitate their feature extraction performance. Therefore, a more robust framework is
developed to enhance pedestrian re-identification which 1) presents a new loss function named
similarity loss designed to generically facilitate feature extraction, 2) presents another loss
function named similarity distillation loss designed to transfer knowledge across training
datasets for reduced labeling effort, and 3) proposes a workflow for what-if facility design
evaluation by integrating pedestrian movement behavior with geometric layout.
Work 2.4: Integrated framework for walkability evaluation. Previous studies of
walkability analyses utilized Building Information Modeling (BIM) and Geographic Information System (GIS) to model infrastructural characteristics, such as the geometric
connectivity of paths. Yet, they lack a comprehensive data schema that formalizes the
walkability-related attributes for analyses among different pedestrian groups and scenarios.
The pedestrian movement behavior is also not fully integrated with infrastructural models, and
a walkability scoring mechanism is also needed to quantitatively analyze walkability of
different routes. Therefore, a framework for walkability evaluation is proposed which 1)
extends the openBIM data schema to formalize the walkability attributes for modeling
individual building, 2) extends the openGIS data schema to define the attributes for
constructing walkability network by integrating BIM, GIS and pedestrian flow data, and 3)
presents a mechanism of walkability scoring to quantify the walking costs of a route
considering different pedestrian groups and walking directions.
Compared to existing approaches of walkability monitoring, the developed methodologies
of CCTV analytics extract pedestrian flow statistics more accurately, with more robust
performance when deployed to different scenes and cameras in practical multi-camera
analytical systems. The methodologies are generic and also provide insight for improving deep
learning models in future research that extract pedestrian movement behavior with CCTV
analytics. Furthermore, the proposed BIM-GIS framework formalizes the data requirement and
modeling methodology of infrastructural attributes, which provide a basis for future walkability
studies when modeling as-built environment. The developed walkability scoring mechanism
also provide a basis for future studies to quantitatively analyze the walkability of different areas.
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