A New Gradient-based Feature Extraction Method for Real-time Detection of Moving Objects Using Stereo Cameras

Abstract

In this study, a gradient-based feature extraction method has been developed that can be used to detect moving objects in real-time applications such as unmanned ground or air vehicles. Feature extraction methods should produce fast results in real-time applications, as results need to be obtained between successive frames of video sequences within a limited time. For this reason, various sized image blocks were used in the developed method. The arithmetic mean (AM), geometric mean (GM), median (MD), and local contrast (LC) methods were used to calculate block intensities. In the stereo video stream, depth maps were also divided into blocks along with successive frames' R, G, and B channels. A novel feature extraction method was developed by calculating gradient-based relationships between adjacent blocks around the centre block. In experimental studies, the features extracted from stereo video frames using the proposed method were compared with Surf, Fast and Brisk methods according to their quantity, accuracy, and processing times, and more successful results were obtained. In addition, the moving object detection performance of the method was tested in real-time using an Unmanned Ground Vehicle.