Feature flow-based abnormal event detection using a scene-adaptive cuboid determination method

As closed circuit television which had been used only for surveillance or identification has developed rapidly the research on intelligent surveillance systems is getting increased interest. Above all, abnormal event detection is becoming an essential part of surveillance systems by detecting or identifying actions or situations which are not commonly occurred in general. In this work, we propose an abnormal event detection method using trajectory modeling with an automatic scene-adaptive cuboid determination scheme. First, we constructed a human appearance model to determine the human size without using any detection method. Then, HOG feature extracted from human images which is the predetermined input is used to construct a human appearance model. We applied a background subtraction to input datasets and then compared HOG feature extracted from the bounding box of the foreground with the human appearance model. The human size is determined by the size of the foreground bounding box with the highest similarity. With the ratio obtained through the experiments, the cuboid size is calculated according to the human size and histogram of oriented tracklets model is constructed by the cuboid size. We used the UCSD dataset to validate the proposed approach. From the experimental results, we verified the significance of the proposed AED method adopting the automatic scene-adaptive cuboid size determination scheme.