@inproceedings{4d4595813c014b549bc1dc2ac1243c49,
title = "Front Moving Object Behavior Prediction System Exploiting Deep Learning Technology for ADAS Applications",
abstract = "This paper proposes a front pedestrian crossing and vehicle cut-in prediction system based on 3D convolution behavior prediction network. The proposed design improves the original 3D convolution network (C3D) to make behavior recognition network have the ability of object localization, which is important to detect multiple moving object behaviors. The proposed system is implemented on the embedded system in real-time, which achieves 20 frames per second when it is deployed on NVIDIA Jetson AGX Xavier and possesses over 92.8% accuracy for pedestrian crossing and 94.3% accuracy for vehicle cut-in behavior detection.",
keywords = "behavior recognition, deep learning, Pedestrian detection, vehicle cut-in",
author = "Tsai, {Wen Chia} and Chen, {Kuan Chou} and Lai, {Jhih Sheng} and Guo, {Jiun In}",
year = "2020",
month = aug,
doi = "10.1109/MWSCAS48704.2020.9184578",
language = "English",
series = "Midwest Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1052--1055",
booktitle = "2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings",
address = "United States",
note = "63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020 ; Conference date: 09-08-2020 Through 12-08-2020",
}