Consumer video dataset with marked head trajectories

Content-based test video corpora usually builds on top of professional material or controlled settings. However, recent years have shown strong increase in user-generated content on the web. The increase in content volume creates challenges in accessibility and utility of the video content. In order to improve the utility of the user-generated videos, better automation for content-based descriptions are needed. Proper test sets are required to develop robust methods for content analysis. Detecting people from video is a common feature that is often seen in both in science and in commercial services. Unfortunately there is a lack of test data for person tracking from consumer videos. In this paper, we introduce a novel video dataset to accommodate this shortage. The dataset is done with two consumer-priced devices: a handheld camcorder and a mobile phone. Both devices were used to store material in indoor and outdoor settings with different attention levels from the people being filmed. The dataset comes with ground truth data that includes person head trajectories and other people marked in the background in MPEG-7-based metadata model. We give description of this metadata model and publish an annotation tool we used for creating the ground truth data. Also, we provide experimental results as a benchmark for all those who would like to use our dataset.