Proceedings ArticleDOI
Comparison of HOG, LBP and Haar-Like Features for On-Road Vehicle Detection
Ashwin Arunmozhi,Jungme Park +1 more
- pp 0362-0367
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TLDR
The detection results show that for the same dataset, LBP features perform better than the other two feature types with a higher detection rate, and a unique and robust detection algorithm using a combination of all the three different feature descriptors and AdaBoost cascade classification is proposed.Abstract:
Autonomous vehicles may be the most significant innovation in transportation since automobiles were first invented. Environmental perception plays a pivotal role in the development of self-driving vehicles which need to navigate in a complex environment of static and dynamic objects. It is required to extract dynamic objects like vehicles and pedestrians more precisely and robustly to estimate the current position, motion and predict its future position. In this article, the performance of three commonly used object detection approaches, Histogram of Oriented Gradients (HOG), Haar-like features and Local Binary Pattern (LBP) is investigated and analyzed using a public dataset of camera images. The detection results show that for the same dataset, LBP features perform better than the other two feature types with a higher detection rate. Finally, a unique and robust detection algorithm using a combination of all the three different feature descriptors and AdaBoost cascade classification is proposed.read more
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Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model.
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Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera
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Vehicle-Related Scene Understanding Using Deep Learning
TL;DR: This is the first time that the authors' traffic environment is utilized as an object for scene understanding based on deep learning, and automatic scene segmentation and object detection are joined for traffic scene understanding.
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Applying the Haar-cascade Algorithm for Detecting Safety Equipment in Safety Management Systems for Multiple Working Environments
TL;DR: In order to create a safety warning system for the working site, the machine-learning algorithm—Haar-cascade classifier—was used to build four different classes for safety equipment recognition and a proposed algorithm was applied to calculate a score to determine the dangerousness of the current working environment.
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A hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery
TL;DR: A novel hybrid method to take advantage of the semantic segmentation of high-resolution airborne imagery to ACE that is realized based on the combination of deep convolutional neural networks and restricted Boltzmann machine (RBM).
References
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