RGBT tracking based on prior least absolute shrinkage and selection operator and quality aware fusion of deep and handcrafted features

Abstract

Object tracking has always been one of the most challenging topics in machine vision. In recent years, trackers have used RGB-T datasets to overcome the limitations of single-modality data. This paper introduces a Prior Least Absolute Shrinkage and Selection Operator (PLASSO)-based cost function optimized by the Alternating Direction Method of Multipliers (ADMM) for a Discriminative Correlation Filter (DCF)-based tracker. Detailed closed-form solutions for these optimization problems are also given in this paper. The proposed PLASSO-based approach adaptively determines the scale, parameters, and features of the PLASSO-ADSPF tracker. PLASSO- ADSPF first finds the response map in the Fourier domain for the input images and then uses an efficient adaptive function to fuse the results. Based on the response maps, the proposed tracker adaptively and consciously extracts features (handcraft and Deep Features (DF)) to improve the speed of the tracking algorithm. PLASSO-ADSPF extracts DF from the convolution layer output of a modified pre-trained deep network via SoftMax Tsallis Entropy (TsEn) as a proposed cost function in the last layer. Also, a graph is proposed to determine longitudinal and transverse scales independently and accurately. Moreover, a new bounding box regression is proposed based on PLASSO and Cholesky factorization to refine the target box. Extensive experiments have been performed for the PLASSO-ADSPF tracker on five popular datasets. The results show the superiority of the proposed approach compared to the state-of-the-art trackers. Our code is available at PLASSO-ADSPF-tracker.