Deformable Patch-based NCC Measure for Visual Tracking

Abstract

This paper tries to solve the visual tracking problem in a simple and intuitive way. In particular, we extend the traditional NCC measure with deformable part-based models (DPM) to get a new image similarity measurement and feed it into the particle filtering framework for visual object tracking. The proposed target model consists of a root template and a set of patch templates. The root is the whole target image, and patches are sub-regions of the target image. NCC responses are calculated for the root template with test images, and every patch template with their corresponding sub-regions of test images. The likelihood between the target and a test image is calculated by integrating all template responses. Instead of directly accumulating all responses, we employ the idea of DPM that combines global and local responses using an elastic model to get a robust image likelihood measure. We also explore an exhaustive patch representation mechanism by introducing the dense NCC feature map. The proposed feature map enables the usage of all patches in the target region for target description and similarity measure. Finally, we provide both qualitative and quantitative experiments against state-of-the-arts on the TB50 dataset. Experimental results show that the proposed similarity measure is effective for visual tracking under challenging scenarios.