124. BAYESIAN MODEL ADAPTATION FOR CROWD COUNTS
Name: Bo Liu
Grad Year: 2020
The problem of transfer learning is considered in the domain of crowd counting. A solution based on Bayesian model adaptation of Gaussian processes is proposed. This is shown to produce intuitive model updates, which are tractable, and lead to an adapted model (predictive dis- tribution) that accounts for all information in both train- ing and adaptation data. The new adaptation procedure achieves significant gains over previous approaches, based on multi-task learning, while requiring much less computa- tion to deploy. This makes it particularly suited for the prob- lem of expanding the capacity of crowd counting camera networks. A large video dataset for the evaluation of adap- tation approaches to crowd counting is also introduced. This contains a number of adaptation tasks, involving infor- mation transfer across video collected by 1) a single camera under different scene conditions (different times of the day) and 2) video collected from different cameras. Evaluation of the proposed model adaptation procedure in this dataset shows good performance in realistic operating conditions.