ACTIVE OBJECT TRACKING USING REINFORCEMENT LEARNING
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Date
2022-05
Authors
Alimzhanov, Bexultan
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
The concept of "smart cities" has rapidly emerged as the means by which urban planners
can improve the quality of life of citizens, providing better services at lower cost.
Typical objectives include the optimization of traffic routing, the automatic detection
of emergency "events" and related improvement in the response time of emergency
services, and overall optimization of resource allocation and energy consumption. A
core component of the smart city concept is the widespread deployment of closedcircuit
cameras for purposes of monitoring and event detection. A typical application
is to locate and track a vehicle as it moves through crowded urban scenarios. Usually,
tracking and camera control tasks are separated, which induces problems for
the construction of a coherent system. Reinforcement learning can be used to unify
the systems, such that control and tracking can be resolved simultaneously. However,
there are issues related to the collection and use of comprehensive real-world data sets
for purposes of research. To avoid this problem, it is feasible to conduct the agent
training using synthetic data, and then transfer the results to real-world settings.
This approach also serves to address the issue of domain invariance. For the thesis,
I investigate active object tracking using reinforcement learning by first developing a
synthetic environment based on the videogame Cities: Skylines, using the extensive
Unity engine, which accurately simulates vehicle traffic in urban settings. The complete
system consisting of a trained object detector and a reinforcement learning agent
is tuned in this environment with corresponding reward functions and action space.
The resulting agent is capable of tracking the objects in the scene without relying on
domain-specific data, such as spatial information. The thesis includes the creation of
the synthetic environment, the development of the agent, and the evaluation of the
resulting system.
Description
Keywords
Type of access: Gated Access, smart cities, Active Object Tracking, Reinforcement Learning, Tracking, Deep Deterministic Policy Gradient, DDPG
Citation
Alimzhanov, B. (2022). Active Object Tracking Using Reinforcement Learning (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan