Degree Name

Doctor of Philosophy


School of Electrical, Computer and Telecommunications Engineering


Many applications need to deal with a scene that evolves over time. This thesis addresses the dynamic scene recognition problem. Video-based dynamic scene recognition methods and mid-level representations are comprehensively reviewed, followed by several proposed methods. First, a part-based approach for dynamic scene recognition is presented. In this approach, representative parts are located by clustering local convolutional features extracted using a pre-trained Fast RCNN model. A set cover problem is then formulated to select the discriminative parts, which are subsequently refined. Local features from a video segment can be extracted and aggregated. Extensive experiments show that this approach is very competitive with state-of-the-art methods that use global features, achieving an overall accuracy of 91.5% and 98.3%, respectively, on two benchmark datasets— the Maryland and the Yupenn.



Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.