Adaptive Weighted CNN Features Integration for Correlation Filter Tracking

Visual object tracking is an active and challenging research topic in computer vision, as objects often undergo significant appearance changes caused by occlusion, deformation, and background clutter. Although convolutional neural network (CNN)-based trackers have achieved competitive results, there are still some limitations. Most existing CNN-based trackers track the object by leveraging high-level semantic features of the highest convolutional layer, which may lead to low-spatial resolution feature maps and degrade the localization precision of tracking. Furthermore, these trackers hardly benefit from end-to-end training since the extraction of features and the learning of classifier are separated. To deal with the above-mentioned issues, we design an adaptive weighted CNN features-based Siamese network for tracking. To capture spatial and semantic information of the object, we design a feature extraction network that derives feature maps by concatenating features of all convolutional layers. To make the features representation more discriminative, we propose a feature integration network. In the feature integration network, we propose a holistic-part network to capture strong visual cues and learn the semantic relations between the holistic object and its parts and combine the holistic-part network with spatial and channel attention mechanisms to adaptively assign weights to each region and channel of the feature maps. In addition, the designed Siamese network can be trained offline end-to-end. The experimental results on the benchmark datasets OTB50 and OTB100 demonstrate that the proposed tracker achieves favorable performance against several state-of-the-art trackers while running at an average speed of 20.5 frames/s.