Deep convolutional particle filter for visual tracking
Reza Jalil Mozhdehi, Henry Ponti Medeiros · 2017
This article proposes a novel framework for visual tracking based on the integration of a deep convolutional neural network (CNN) and a particle filter. In the proposed framework, the position of the target at each frame is predicted by a particle filter according to a motion model. Particles around the predicted position are then used as input to the HCFT CNN-based tracker which adjusts their positions to the most likely target positions. The weights of the particles are then determined using the correlation map of the CNN tracker. Finally, the particles and their weights are used to calculate the position of the target in the current frame. We evaluated the performance of the proposed framework using the Visual Tracker Benchmark v1.0. Our results show that this method improves the performance of HCFT in challenging attributes such as deformation, illumination, out-of-plane and in-plane rotations, as well as overall performance.