Deep Convex-Nonnegative Matrix Factorization Integrated with Deep Convolutional Networks for On-Road Obstacle Detection

Due to the fact that the number of on-road accidents increases over years, developing an advanced driver assistance system (ADAS) is getting to be critical. The ADAS is a system which applies advanced computer technologies to alert drivers at the appropriate timing to minimize the possibilities of accidents. The most essential part of ADAS is to detect any on-road obstacles through captured visual images that may jeopardize the running host vehicle especially from its front. In this thesis, we propose a novel deep learning framework which incorporates our proposed Deep Convex-Non-negative Matrix Factorization (DC-NMF) technique to process the camera images for obstacle detection. Besides this, our proposed novel model, called Deep Convex-NMF (DC-NMF), which helps one to learn more sophisticated bases that represent the original high dimensional features. Logically, we first use this aforementioned model to extract multilayer basis matrix and then use it to improve the detection performance of the proposed novel deep learning framework, or called Deep Convex-NMF Net (DC-NMF Net). To validate the proposed work, we evaluate the AP of our proposed method on KITTI and INRIA dataset, and we find that the respective quantitative performances are 79% and 91%. We also establish our own urban scene dataset and test the performance of our method on it which turns out to be able to achieve 95% recall/precision.