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