Real-time small obstacle detection on highways using compressive RBM road reconstruction

Small objects on the road can become hazardous obstacles when driving at high speed. Detecting such obstacles is vital to guaranty the safety of self-driving car users, especially on highways. Such tasks cannot be performed using existing active sensors such as radar or LIDAR due to their limited ra...

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Bibliographic Details
Published in2015 IEEE Intelligent Vehicles Symposium (IV) pp. 162 - 167
Main Authors Creusot, Clement, Munawar, Asim
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2015
Subjects
Heating
Image coding
Image reconstruction
Roads
Sensors
Training
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Summary:Small objects on the road can become hazardous obstacles when driving at high speed. Detecting such obstacles is vital to guaranty the safety of self-driving car users, especially on highways. Such tasks cannot be performed using existing active sensors such as radar or LIDAR due to their limited range and resolution at long distances. In this paper we propose a technique to detect anomalous patches on the road from color images using a Restricted Boltzman Machine neural network specifically trained to reconstruct the appearance of the road. The differences between the observed and reconstructed road patches yield a more relevant segmentation of anomalies than classic image processing techniques. We evaluated our technique on texture-based synthetic datasets as well as on real video footage of anomalous objects on highways.
ISSN:1931-0587
2642-7214
DOI:10.1109/IVS.2015.7225680