Efficient Multi-level Correlating for Visual Tracking
This addresses the need for real-time, accurate visual tracking in applications like surveillance or robotics, though it is incremental as it builds on existing correlation filter methods.
The paper tackles the trade-off between speed and accuracy in correlation filter-based visual tracking by proposing MLCFT, a multi-level approach with two-stage detection and feature fusion, achieving state-of-the-art performance at over 16 frames per second.
Correlation filter (CF) based tracking algorithms have demonstrated favorable performance recently. Nevertheless, the top performance trackers always employ complicated optimization methods which constraint their real-time applications. How to accelerate the tracking speed while retaining the tracking accuracy is a significant issue. In this paper, we propose a multi-level CF-based tracking approach named MLCFT which further explores the potential capacity of CF with two-stage detection: primal detection and oriented re-detection. The cascaded detection scheme is simple but competent to prevent model drift and accelerate the speed. An effective fusion method based on relative entropy is introduced to combine the complementary features extracted from deep and shallow layers of convolutional neural networks (CNN). Moreover, a novel online model update strategy is utilized in our tracker, which enhances the tracking performance further. Experimental results demonstrate that our proposed approach outperforms the most state-of-the-art trackers while tracking at speed of exceeded 16 frames per second on challenging benchmarks.