PEDESTRIAN ACTIVITY PREDICTION BASED ON SEMANTIC SEGMENTATION AND HYBRID OF MACHINES
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DOI:
https://doi.org/10.15625/1813-9663/34/2/12655Keywords:
Deep learning, Pedestrian recognition, Semantic segmentation, Feature extraction, Object detection, Autonomous vehicleAbstract
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.Metrics
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Published
03-10-2018
How to Cite
[1]
D.-P. Tran, V.-D. Hoang, T.-C. PHAM, and C.-M. LUONG, “PEDESTRIAN ACTIVITY PREDICTION BASED ON SEMANTIC SEGMENTATION AND HYBRID OF MACHINES”, JCC, vol. 34, no. 2, pp. 113–125, Oct. 2018.
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Computer Science
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