GEOMETRIC DEEP PARTICLE FILTER FOR MOTORCYCLE TRACKING: DEVELOPMENT OF INTELLIGENT TRAFFIC SYSTEM IN JAKARTA

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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VOLUME 8 , ISSUE 1 (March 2015) > List of articles

GEOMETRIC DEEP PARTICLE FILTER FOR MOTORCYCLE TRACKING: DEVELOPMENT OF INTELLIGENT TRAFFIC SYSTEM IN JAKARTA

Alexander A S Gunawan * / Wisnu Jatmiko *

Keywords : visual tracking, motorcycle, nonretinotopic, particle filter, deep learning, geometric computing, affine transformation.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 1, Pages 429-463, DOI: https://doi.org/10.21307/ijssis-2017-766

License : (CC BY-NC-ND 4.0)

Received Date : 04-October-2014 / Accepted: 20-January-2015 / Published Online: 01-March-2015

ARTICLE

ABSTRACT

Intelligent Transportation Systems (ITS) is the combination of transportation systems with Information and Communication Technology (ICT). In Jakarta traffic, there is unique issue that does not arise in developed countries: very large number of motorcycles. Nevertheless, the enabling technologies for the detection, measurement, recording, and information distribution of motorcycle have not been fully developed in the existing researches. With the above considerations, we establish research which aimed to develop enabling technology especially in here for tracking motorcycle using camera. This paper is presented our proposed tracker which called as Geometric Deep Particle Filter (GDPF) for tracking motorcycle using camera. The tracker is inspired by human visual perception which has nonretinotopic nature. Based on particle filter approach, our goal is to improve the transition model in order to overcome motorcycle maneuver. We will exploit this curved nature of the state space using geometric computing theory, such as Lie groups, and Lie algebras. A number of experiments have been conducted for this research, and it has been found that GDPF has achieved certain degree of success in object tracking.

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