Example-based Visual Object Counting for complex background with a local low-rank constraint

Abstract

Visual object counting (VOC) is important in many real-world applications. Our previous work approximated sparsity-constrain example-based VOC (ASE-VOC) works well with insufficient training data. It assumes that image patches share the similar local geometry with counterpart density maps, and then the density map of the image patch can be estimated by preserving such geometry. However. ASE-VOC has a weak constraint for data structure and experiments reveal that the performance of ASE-VOC degrades when facing with complex background. To solve this problem, we proposed a novel local low-rank constrained example-based VOC (LLRE-VOC) method. Because local low-rank constraint can choose the samples belonging to the subspace that lies closest to the test samples. Even with complicated data structure, LLRE-VOC can guarantee the patches selected share similar structure with input patch. Extensive experiments conducted on public benchmarks demonstrate the superior performance of our proposed LLRE-VOC method.