Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/16592
Title: AMMDAS: Multi-modular generative masks processing architecture with adaptive wide field-of-view modeling strategy
Authors: Desanamukula V.S.
Chilukuri P.K.
Padala P.
Padala P.
Pvgd P.R.
Issue Date: 2020
Citation: IEEE Access , Vol. 8 , , p. 194748 - 198778
Abstract: The usage of transportation systems is inevitable; any assistance module which can catalyze the flow involved in transportation systems, parallelly improving the reliability of processes involved is a boon for day-to-day human lives. This paper introduces a novel, cost-effective, and highly responsive Post-active Driving Assistance System, which is "Adaptive-Mask-Modelling Driving Assistance System" with intuitive wide field-of-view modeling architecture. The proposed system is a vision-based approach, which processes a panoramic-front view (stitched from temporal synchronous left, right stereo camera feed) & simple monocular-rear view to generate robust & reliable proximity triggers along with co-relative navigation suggestions. The proposed system generates robust objects, adaptive field-of-view masks using FRCNN+Resnet-101_FPN, DSED neural-networks, and are later processed and mutually analyzed at respective stages to trigger proximity alerts and frame reliable navigation suggestions. The proposed DSED network is an Encoder-Decoder-Convolutional-Neural-Network to estimate lane-offset parameters which are responsible for adaptive modeling of field-of-view range (1570-2100) during live inference. Proposed stages, deep-neural-networks, and implemented algorithms, modules are state-of-the-art and achieved outstanding performance with minimal loss(L{p, t}, Lδ, LTotal) values during benchmarking analysis on our custom-built, KITTI, MS-COCO, Pascal-VOC, Make-3D datasets. The proposed assistance-system is tested on our custom-built, multiple public datasets to generalize its reliability and robustness under multiple wild conditions, input traffic scenarios & locations. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
URI: https://doi.org/10.1109/ACCESS.2020.3033537
http://idr.nitk.ac.in/jspui/handle/123456789/16592
Appears in Collections:1. Journal Articles

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