Vehicle-pedestrian classification with road context recognition using convolutional neural networks
College
Gokongwei College of Engineering
Department/Unit
Manufacturing Engineering and Management
Document Type
Conference Proceeding
Source Title
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018
Publication Date
3-12-2019
Abstract
In road traffic scene analysis, it is important to observe vehicular traffic and how pedestrian foot traffic affects the over-all traffic situation. Road context is also significant in proper detection of vehicles and pedestrians. This paper presents a vehicle-pedestrian detection and classification system with road context recognition using convolutional neural networks. Using Catch-All traffic video data sets, the system was trained to identify vehicles and pedestrians in four different road conditions such as low-altitude view T-type road intersection (DSO), mid-altitude view bus stop area in day-time (DS4-1) and night-time (DS4-3) condition, and high-altitude view wide intersection (DS3-1). In the road context recognition, the system was first tasked to identify in which of the four road conditions the current traffic scene belongs. This is designed to ensure a high detection rate of vehicles and pedestrians in the mentioned road conditions. Road context recognition has 98.64% training accuracy with 2800 sample images, and 100% validation accuracy with 1200 sample images. After road context recognition, a detection algorithm for vehicle and pedestrians was trained for each condition. In DSO, the training accuracy is 97.75% with 1200 image samples, while validation accuracy is 94.75% with 400 image samples. In DS3-1, the training accuracy is 98.63% with 1400 image samples, while validation accuracy is 98.29% with 600 image samples. In DS4-1, the training accuracy is 99.43% with 1400 image samples, while validation accuracy is 99.83% with 600 image samples. In DS4-3, the training accuracy is 97.77% with 1400 image samples, while validation accuracy is 98.29% with 600 image samples. © 2018 IEEE.
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Digitial Object Identifier (DOI)
10.1109/HNICEM.2018.8666257
Recommended Citation
Billones, R. C., Bandala, A. A., Gan Lim, L. A., Culaba, A. B., Vicerra, R. P., Sybingco, E., Fillone, A. M., & Dadios, E. P. (2019). Vehicle-pedestrian classification with road context recognition using convolutional neural networks. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018 https://doi.org/10.1109/HNICEM.2018.8666257
Disciplines
Manufacturing | Mechanical Engineering
Keywords
Vehicle detectors; Pedestrian inertial navigation systems; Context-aware computing; Sensor networks; Traffic flow
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