This paper addresses the problem of vehicle classification using laser scanner profiles, which is usually found as a component of electronic tolling systems. Laser scanners obtain a 3D measurement of the vehicle surface. Previous approaches treated the laser scans as images. In addition to high-level features (such as width, height, length and other measurements) from the scanner profiles, feature descriptors have been used for supervised classification of laser scanner profiles. This allowed to deploy a generic approach to the image classification based on Fisher features. In this paper we adopt a different approach which extracts and analyses vehicle shapes from the laser scans.
The method proceeds to finding typical vehicle shapes and classifying them through the global shape alignment.
Vehicle Type classification from laser Scans with Global Alignment Kernels
| Boris Chidlovskii, Gabriela Csurka, José A. Rodriguez |
| 17th International IEEE Conference on Intelligent Transportation Systems, Qingdao, China, October 8-11, 2014 |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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