Deep Reinforcement Learning-Based DQN Agent Algorithm for Visual Object Tracking in a Virtual Environmental Simulation

The complexity of object tracking models among hardware applications has become a more in-demand task to accomplish with multifunctional algorithm skills in various indeterminable environment tracking conditions. Experimenting with the virtual realistic simulator brings new dependencies and requirements, which may cause problems while experimenting with runtime processing. The goal of this paper is to present an object tracking framework that differs from the most advanced tracking models by experimenting with virtual environment simulation (Aerial Informatics and Robotics Simulation—AirSim, City Environ) using one of the Deep Reinforcement Learning Models named as Deep Q-Learning algorithms. Our proposed network examines the environment using a deep reinforcement learning model to regulate activities in the virtual simulation environment and utilizes sequential pictures from the realistic VCE (Virtual City Environ) model as inputs. Subsequently, the deep reinforcement network model was pretrained using multiple sequential training image sets and fine-tuned for adaptability during runtime tracking. The experimental results were outstanding in terms of speed and accuracy. Moreover, we were unable to identify any results that could be compared to the state-of-the-art methods that use deep network-based trackers in runtime simulation platforms, since this testing experiment was conducted on the two public datasets VisDrone2019 and OTB-100, and achieved better performance among compared conventional methods

Deep Reinforcement Learning Tf-Agent-Based Object Tracking With Virtual Autonomous Drone in a Game Engine

The recent development of object-tracking frameworks has affected the performance of many manufacturing and industrial services such as product delivery, autonomous driving systems, security systems, military, transportation and retailing industries, smart cities, healthcare systems, agriculture, etc. Achieving accurate results in physical environments and conditions remains quite challenging for the actual object-tracking. However, the process can be experimented with using simulation techniques or platforms to evaluate and check the model’s performance under different simulation conditions and weather changes. This paper presents one of the target tracking approaches based on the reinforcement learning technique integrated with TensorFlow-Agent (tf-agent) to accomplish the tracking process in the Unreal Game Engine simulation platform AirSim Blocks. The productivity of these platforms can be seen while experimenting in virtual-reality conditions with virtual drone agents and performing fine-tuning to achieve the best or desired performance. In this paper, the tf-agent drone learns how to track an object integration with a deep reinforcement learning process to control the actions, states, and tracking by receiving sequential frames from a simple Blocks environment. The tf-agent model is trained in the AirSim Blocks environment for adaptation to the environment and existing objects in a simulation environment for further testing and evaluation regarding the accuracy of tracking and speed. We tested and compared two approaches, DQN and PPO trackers, and reported results in terms of stability, rewards, and numerical performance

Abstracts of 1st International Conference on Machine Intelligence and System Sciences

This book contains the abstracts of the papers presented at the International Conference on Machine Intelligence and System Sciences (MISS-2021) Organized by the Techno College of Engineering, Agartala, Tripura, India & Tongmyong University, Busan, South Korea, held on 1–2 November 2021. This conference was intended to enable researchers to build connections between different digital technologies based on Machine Intelligence, Image Processing, and the Internet of Things (IoT). Conference Title: 1st International Conference on Machine Intelligence and System SciencesConference Acronym: MISS-2021Conference Date: 1–2 November 2021Conference Location: Techno College of Engineering Agartala, Tripura(w), IndiaConference Organizer: Techno College of Engineering, Agartala, Tripura, India & Tongmyong University, Busan, South Korea