Features Identification and Tracking for an Autonomous Ground Vehicle

This thesis attempts to develop features identification and tracking system for an autonomous ground vehicle by focusing on four fundamental tasks: Motion detection, object tracking, scene recognition, and object detection and recognition. For motion detection, we combined the background subtraction method using the mixture of Gaussian models and the optical flow to highlight any moving objects or new entering objects which stayed still. To increase robustness for object tracking result, we used the Kalman filter to combine the tracking method based on the color histogram and the method based on invariant features. For scene recognition, we applied the algorithm Census Transform Histogram (CENTRIST), which is based on Census Transform images of the training data and the Support Vector Machine classifier, to recognize a total of 8 scene categories. Because detecting the horizon is also an important task for many navigation applications, we also performed horizon detection in this thesis. Finally, the deformable parts-based models algorithm was implemented to detect some common objects, such as humans and vehicles. Furthermore, objects were only detected in the area under the horizon to reduce the detecting time and false matching rate.