Deep Convolutional Feature Histograms for Visual Object Tracking

Abstract

Visual Object Tracking remains an open and challenging task in the Computer Vision field, requiring tracking algorithms to achieve a feeble balance between precision and speed performance. In this work, inspired by the classic Mean Shift algorithm for object tracking using histograms, as well as the recent advances of deep Convolutional Neural Networks (CNNs), we propose a novel tracker that incorporates elements from both worlds. Our tracker uses a deep CNN as the feature extraction backbone, which is capable of extracting semantically meaningful features from the target and its background, as well as a fully learnable Bag-of-Features mechanism which extracts histograms from those features. The tracker operates in a fully-convolutional fashion, allowing for the direct and efficient evaluation of multiple possible target locations. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed tracker, allowing it to run at high speeds even on systems with lower computational capacity.