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Pedestrian activity prediction based on semantic segmentation and hybrid of machines

The article presents an advanced driver assistance system (ADAS) based on a situational recognition solution and provides alert levels in the context of actual traffic. The solution is a process in which a single image is segmented to detect pedestrians’ position as well as extract features of pedestrian posture to predict the action. | Journal of Computer Science and Cybernetics, V.34, N.2 (2018), 113–125 DOI 10.15625/1813-9663/34/2/12655 PEDESTRIAN ACTIVITY PREDICTION BASED ON SEMANTIC SEGMENTATION AND HYBRID OF MACHINES DIEM-PHUC TRAN1 , VAN-DUNG HOANG2,a , TRI-CONG PHAM3 , CHI-MAI LUONG3,4 1 Duy Tan University Binh University 3 ICTLab, University of Science and Technology of Hanoi 4 Institute of Information Technology, VAST a dunghv@qbu.edu.vn 2 Quang Abstract. The article presents an advanced driver assistance system (ADAS) based on a situational recognition solution and provides alert levels in the context of actual traffic. The solution is a process in which a single image is segmented to detect pedestrians’ position as well as extract features of pedestrian posture to predict the action. The main purpose of this process is to improve accuracy and provide warning levels, which supports autonomous vehicle navigation to avoid collisions. The process of the situation prediction and issuing of warning levels consists of two phases: (1) Segmenting in order to definite the located pedestrians and other objects in traffic environment, (2) Judging the situation according to the position and posture of pedestrians in traffic. The accuracy rate of the action prediction is 99.59% and the speed is 5 frames per second. Keywords. Autonomous vehicle, deep learning, feature extraction, object detection, pedestrian recognition, semantic segmentation. 1. INTRODUCTION Nowadays, recognition technology on autonomous vehicle (AV) is widely applied in real life. For AV, basic objects have been recognized with high accuracy and specific handling situations. However, of all subjects interacting with AVs in actual traffic, pedestrians are considered to be the most difficult to identify and handle. Consequently, the combination of multiple methods to improve the efficiency in predicting and conducting different levels of classification is absolutely necessary. When a pedestrian joins traffic on the road, .