A Scale-Adaptive Particle Filter Tracking Algorithm Based on Offline Trained Multi-Domain Deep Network

Abstract

In this paper, a MDSPF method is proposed to learn a robust observation model for representing the targets by training a CNN with a number of video sequences. The CNN architecture is composed of three shared convolutional units, two shared fully connected (Fc) units and a multiple domain Fc unit, and it is offline trained by a multi-domain learning strategy. After training, the shared convolutional units are remained as an observation model for our tracking framework. The features from the shared convolutional units can well adapt to the challenges in tracking tasks. A scale-adaptive particle filter is also proposed in our framework to improve the robustness of particle filter method. Different from most existing particle filter tackers, it can efficiently shepherd each particle towards a more precise location and scale through similarity evaluation. Extensive experiments are conducted on Object Tracking Benchmark (OTB), UAV123 and LaSOT datasets to verify the efficiency of our proposed method.