Deep Convolutional Correlation Iterative Particle Filter for Visual Tracking
This work addresses visual tracking for applications like surveillance or robotics, but it appears incremental as it combines existing components with a novel clustering strategy.
The paper tackles visual tracking by integrating an iterative particle filter with deep convolutional and correlation filters, achieving favorable performance against state-of-the-art methods on two benchmark datasets.
This work proposes a novel framework for visual tracking based on the integration of an iterative particle filter, a deep convolutional neural network, and a correlation filter. The iterative particle filter enables the particles to correct themselves and converge to the correct target position. We employ a novel strategy to assess the likelihood of the particles after the iterations by applying K-means clustering. Our approach ensures a consistent support for the posterior distribution. Thus, we do not need to perform resampling at every video frame, improving the utilization of prior distribution information. Experimental results on two different benchmark datasets show that our tracker performs favorably against state-of-the-art methods.