Cost-sensitive sparse linear regression for crowd counting with imbalanced training data
Xiaolin Huang, Yuexian Zou, Yi Wang · 2016
Video-based crowd counting (VCC) is a high demanded technique in many video applications. Existing supervised VCC methods essentially learn an intrinsic mapping function between image features and corresponding crowd counts. However, imbalanced training dataset degrades the performance of VCC significantly. Encouraged by recent success in cost-sensitive learning for image classification with imbalance dataset, we propose a novel cost-sensitive sparse linear regression VCC method (CS-SLR-VCC). Specifically, a sparse linear regression (SLR) model is firstly learned and the modelling errors associated with each training data are calculated accordingly. Then, aiming to eliminate the adverse effect of the high modelling errors of SLR model due to imbalanced data, all modelling errors are taken as prior knowledge to design sample-related weighting factors. Thus, a cost-sensitive SLR model is reformulated and its optimal solution is derived. Extensive experiments conducted on public UCSD and Mall benchmarks demonstrate the superior performance of our proposed CS-SLR-VCC method.