Visual tracking frameworks employing Convolutional Neural Networks (CNNs) have shown state-of-the-art performance due to their hierarchical feature representation. While classification and update based deep neural net tracking have shown good performance in terms of accuracy, they have poor tracking speed. On the other hand, recent matching based techniques using CNNs show higher than real-time speed in tracking but this speed is achieved at a considerably lower accuracy. To successfully manage the trade-off between accuracy and speed, we propose a novel CNN architecture for visual tracking. We achieve this trade-off balance by using an approach in which consecutive similar frames are processed with a similarity matching technique, and dissimilar frames are processed with a classification approach within the CNN architecture.
The tracking speed is improved by avoiding unnecessary model updates through the measurement of similarity between adjacent frames, while the accuracy is maintained by adopting a classification approach when needed, with deeper level features.
Extensive evaluation performed on a publicly available benchmark dataset demonstrates our proposed tracker shows competitive performance while maintaining near real-time speed.