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Publication Type : Conference Paper
Publisher : IOS Press
Source : Journal of Intelligent and Fuzzy Systems, IOS Press, Volume 36, Number 3, p.1957-1965 (2019)
Keywords : Ability testing, Aerial images, Antennas, Codes (symbols), Computer debugging, Computer vision, Different class, Ground vehicles, High-speed tracking, HTTP, Intelligent systems, Lines of code, Object Detection, Object recognition, Open systems, Portals, Proof of concept, Small target detection, Soft computing, Source codes, Statistical tests, Vehicle detection
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2019
Abstract : Deep Rectified System for High-speed Tracking in Images (DRSHTI) is a unified open-source web portal developed for object detection in images. It aims to be a platform for the end user, where he/she can perform object detection on images without going through the hassles of debugging countless lines of code or setting up the right environment to perform computer vision tasks. By making the platform open-source, this work targets beginners in computer vision to form a basic understanding of object detection as an artificial intelligence task. This is made possible by releasing source codes, tools and tutorials on its usage via GitHub. This open-source portal offers two detection pipelines based on Faster-RCNN - a model to detect ground vehicles in aerial images and a model to detect everyday objects in 37 different classes in normal images. The former model is trained on VEDAI dataset, which gave 98.6% accuracy during testing and is offered as proof-of-concept that showcases the models ability to perform small target detections, but the latter model is trained on the PASCAL VOC dataset. Making the project open-source also aims at bringing in more development and tweaking to the existing vehicle detection module. The web portal can be accessed via https://drshti.github.io(link is external), where user can upload images and get annotations on objects present in it. Tutorials and source codes can be found at https://github.com/vyzboy92/Object-Detection-Net(link is external). © 2019 - IOS Press and the authors.
Cite this Research Publication : V. S. Mohan, Vinayakumar, R., Sowmya, and Dr. Soman K. P., “Deep rectified system for high-speed tracking in images”, in Journal of Intelligent and Fuzzy Systems, 2019, vol. 36, pp. 1957-1965.