Multi-camera trajectory mining: Database and evaluation

In recent years, large-scale video search and mining has been an active research area. Exploring the trajectory of pedestrian of interest in non-overlapping multi-camera network, namely the trajectory mining, is very useful for visual surveillance and criminal investigation. The trajectory mentioned in our work describes the transition of pedestrian among cameras from a macroscopic perspective which is different from the concept in conventional tracking field. In this paper, we collect a database called TMin to promote research and development of trajectory mining. This release of Version 1 contains 1680 images from 30 subjects, all the images are extracted from 6 surveillance videos over two hours, and each subject appears in at least two different cameras. We describe the apparatuses, environments and procedure of the data collection and present baseline performance on the TMin database.