Object Detection Using FAST Corner Detector Based on Smartphone Platforms

In this paper, we proposed a real-time object recognition system under smart phone environments. The proposed object recognition system consists of two key modules: feature extraction and object recognition. Feature detectors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are good methods which yield high quality features, however they are too computationally intensive for use in real-time applications of any complexity. Compared to PC platforms, smart phone platforms have limited resources, so computation-intensive SIFT and SURF descriptors are less usable in such resource-limited environments. In this paper utilizes the FAST corner detector that provides faster feature computation by extracting only corner information. The number of corners detected by the FAST corner detector varies so normalization is applied to adjust the extracted corners (interest points) to the same number. Based on the normalized corner information, support vector machine (SVM) and back-propagation neural network (BPNN) training are performed for the efficient recognition of objects. Compared to conventional SIFT and SURF algorithms, the proposed object recognition system based on the FAST corner detector yields increased speed and low performance degradation on smart phones.