Conference Paper

Vehicle Detection based on Deep Learning Heatmap Estimation

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Abstract

The detection and localization of objects on point cloud provided by LiDAR sensors has various applications, especially for autonomous driving. As methods are being more efficient in terms of performance and execution speed, they operate in an end-to-end fashion, not allowing in case of errors the evaluation of which areas could cause failures. We propose in this paper a method that delivers an intermediate top view heatmap that will, firstly, facilitates the estimation part to focus on the right areas, and secondly, allow determining which parts of the space are keeping the system’s attention. Vehicles are represented on this map as ellipses. Objects with heavy occlusion may not illustrate a complete ellipse, however their mark may still indicate that a vehicle is hidden, allowing planification algorithms to decide a reduction of the velocity as something may suddenly appear. Furthermore, thanks to the alternative representation of objects, detections can be extracted even from the intermediate map, producing redundancy in a single system. The approach presented in this paper is distinguished by its relative computational simplicity compared to the state-of-art methods. In addition to its implementation simplicity, the proposed architecture reaches high level of performance on the KITTI detection benchmark.

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... One class detects only the vehicle target. Examples include a vehicle detection network based on heatmap estimation [30] and a convolutional neural network with a featured representation [31]. The other class are 3D point cloud semantic segmentation networks, and vehicles are only one category of their segmentation results. ...
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