Evaluation of Classification Algorithms for Road Environment Detection

Authors: T. Anbu, K. Aravind Kumar

Abstract:

The road environment information is needed accurately for applications such as road maintenance and virtual 3D city modeling. Mobile laser scanning (MLS) produces dense point clouds from huge areas efficiently from which the road and its environment can be modeled in detail. Objects such as buildings, cars and trees are an important part of road environments. Different methods have been developed for detection of above such objects, but still there is a lack of accuracy due to the problems of illumination, environmental changes, and multiple objects with same features. In this work the comparison between different classifiers such as Multiclass SVM, kNN and Multiclass LDA for the road environment detection is analyzed. Finally the classification accuracy for kNN with LBP feature improved the classification accuracy as 93.3% than the other classifiers.

Keywords: Classifiers, feature extraction, mobile-based laser scanning, object location estimation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1314558

References:

[1] Valentino, John. A System for Scene Understanding and Classification of Objects in the Road Environment with Application to Autonomous Vehicles. Diss. Princeton University, 2012.
[2] Halgaš, Ján, and Aleš Janota. "Classification of objects of road environment based on point clouds using reflectivity of the laser beam".
[3] Amit, Yali, and Pedro Felzenszwalb. "Object Detection." Computer Vision: A Reference Guide (2014): 537-542.
[4] Zhang, Shunli, Xin Yu, Yao Sui, Sicong Zhao, and Li Zhang. "Object tracking with multi-view support vector machines." IEEE Transactions on Multimedia 17.3 (2015): 265-278.
[5] Karaimer, Hakki Can, Ibrahim Cinaroglu, and Yalin Bastanlar. "Combining shape-based and gradient-based classifiers for vehicle classification." 2015 IEEE 18th International Conference on Intelligent Transportation Systems. IEEE, 2015.
[6] Fernández, Carlos, Rubén Izquierdo, David Fernandez Llorca, and Miguel Angel Sotelo. "A comparative analysis of decision trees based classifiers for road detection in urban environments." 2015 IEEE 18th International Conference on Intelligent Transportation Systems. IEEE, 2015.
[7] Oujaoura, Mustapha, Rachid El Ayachi, Brahim Minaoui, Mohammed Fakir, and Omar Bencharef. "Grouping K-means adjacent regions for semantic image annotation using Bayesian networks." 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV). IEEE, 2016.
[8] Riveiro, Belén, Lucía Díaz-Vilariño, Borja Conde-Carnero, Mario Soilán, and Pedro Arias. "Automatic Segmentation and Shape-Based Classification of Retro-Reflective Traffic Signs from Mobile LiDAR Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9.1 (2016): 295-303.
[9] Hu, Qichang, Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel, and Fatih Porikli. "Fast detection of multiple objects in traffic scenes with a common detection framework." IEEE Transactions on Intelligent Transportation Systems 17.4 (2016): 1002-1014.
[10] Lehtomäki, Matti, et al. "Object Classification and Recognition From Mobile Laser Scanning Point Clouds in a Road Environment." IEEE Transactions on Geoscience and Remote Sensing 54.2 (2016): 1226-1239.