Online object trajectory classification using FPGA-SoC devices

Shinde, P., Machado, P. ORCID: 0000-0003-1760-3871, Santos, F.N. and McGinnity, T.M. ORCID: 0000-0002-9897-4748, 2018. Online object trajectory classification using FPGA-SoC devices. In: UKCI 2018: 18th Annual UK Workshop on Computational Intelligence, Nottingham Trent University, Nottingham, 5-7 September 2-18. Advances in intelligent systems and computing . Springer. (Forthcoming)

Preview

Text 11294_Machado.pdf - Pre-print Download (4MB) | Preview

Abstract

Real time classification of objects using computer vision techniques are becoming relevant with emergence of advanced perceptions systems required by, surveillance systems, industry 4.0 robotics and agricultural robots. Conventional video surveillance basically detects and tracks moving object whereas there is no indication of whether the object is approaching or receding the camera (looming). Looming detection and classification of object movements aids in knowing the position of the object and plays a crucial role in military, vehicle traffic management, robotics, etc. To accomplish real-time object trajectory classification, a contour tracking algorithm is necessary. In this paper, an application is made to perform looming detection and to detect imminent collision on a system-on-chip field-programmable gate array (SoC- FPGA) hardware. The work presented in this paper was designed for running in Robotic platforms, Unmanned Aerial Vehicles, Advanced Driver Assistance System, etc. Due to several advantages of SoC-FPGA the proposed work is performed on the hardware. The proposed work focusses on capturing images, processing, classifying the movements of the object and issues an imminent collision warning on-the-fly. This paper details the proposed software algorithm used for the classification of the movement of the object, simulation of the results and future work.

Item Type:	Chapter in book
Creators:	Shinde, P., Machado, P., Santos, F.N. and McGinnity, T.M.
Publisher:	Springer
Date:	September 2018
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	20 Jun 2018 13:11
Last Modified:	20 Jun 2018 13:27
URI:	https://irep.ntu.ac.uk/id/eprint/33880

Actions (login required)

Edit View

Views

Downloads