Extreme learning machines based image classification and scene characterization for autonomous driving


To develop an efficient Extreme Learning Machine (ELM) based solution for object classification and detection during autonomous driving.  


The project team has developed two state-of-the-art object detectors for performance analysis: namely Faster Region based Convolutional Neural Network (R-CNN) and Hierarchical- Extreme Learning Machine (H-ELM) based object detector. Attributed to its superiority in feature learning and object localisation, the detection performance delivered by Faster R-CNN is more promising compared to H-ELM.Despite the satisfactory result produced by R-CNN, the implementation of the algorithm for practical online application is still limited by its tedious computational effort. The team is currently looking into the possibility of combining both algorithms in a hybrid framework in order to obtain a trade-off between detection performance and computational efficiency. 


                                   Object classification using Convolutional Neural Network (CNN)                                                                   and Hierarchical-Extreme Learning Machine ( H-ELM) in a hybrid framework. 


                                                                     Real-time object detection

Demonstration Video

Principal Investigators

Prof. Huang Guangbin (NTU)

Telephone: 6790 4489
Office: S2.1-B2-06
Mr. Paul Tan (STE)

Telephone: 6660 1052
Office: S1-B4a-03