Accelerate High Resolution Image Pedestrian Detection With Non-Pedestrian Area Estimation

ZHANG, Haodi; Pop, Danut Ovidiu; Rogozan, Alexandrina; Bensrhair, Abdelaziz

doi:10.1109/access.2021.3049401

Cited by 6 publications

(1 citation statement)

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“…In [ 19 ], authors provide a dataset for the classification of pedestrian movements and their intentions according to their motion direction using deep learning techniques ( , accessed on 15 October 2021). New recent research is related to detect pedestrians on high-speed roads using high-resolution images [ 20 ] showing that ADAS is of great importance for preventing collisions especially when autonomous vehicles are considered.…”

Section: Related Workmentioning

confidence: 99%

Communication Network Architectures for Driver Assistance Systems

Giuliano

Mazzenga

Innocenti

et al. 2021

Sensors

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Autonomous Driver Assistance Systems (ADAS) are of increasing importance to warn vehicle drivers of potential dangerous situations. In this paper, we propose one system to warn drivers of the presence of pedestrians crossing the road. The considered ADAS adopts a CNN-based pedestrian detector (PD) using the images captured from a local camera and to generate alarms. Warning messages are then forwarded to vehicle drivers approaching the crossroad by means of a communication infrastructure using public radio networks and/or local area wireless technologies. Three possible communication architectures for ADAS are presented and analyzed in this paper. One format for the alert message is also presented. Performance of the PDs are analyzed in terms of accuracy, precision, and recall. Results show that the accuracy of the PD varies from 70% to 100% depending on the resolution of the videos. The effectiveness of each of the considered communication solutions for ADAS is evaluated in terms of the time required to forward the alert message to drivers. The overall latency including the PD processing and the alert communication time is then used to define the vehicle braking curve, which is required to avoid collision with the pedestrian at the crossroad.

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Section: Related Workmentioning

confidence: 99%