Part-Based Tracking via Salient Collaborating Features

Abstract

We present a novel part-based method for model-free tracking. In our model, key points are considered as elementary predictors, collaborating to localize the target. In order to differentiate reliable features from outliers and bad predictors, we define the notion of feature saliency including three factors: the persistence, the spatial consistency, and the predictive power of local features. Saliency information is learned during tracking to be used in several algorithmic steps: local predictions, global localization, feature removal, etc. By exploiting saliency information and key point structural properties, the proposed algorithm is able to track accurately generic objects, facing several difficulties such as occlusions, presence of distractors, and abrupt motion. The proposed tracker demonstrated a high robustness on challenging public datasets, outperforming significantly five recent state-of-the-art trackers.