Multi-Task Probabilistic Regression With Overlap Maximization for Visual Tracking

Recent researches made a breakthrough in visual tracking accuracy. Many trackers benefit from the object state representations and network loss functions, which mine the output space and improve the power of supervision, respectively. Probabilistic regression method models the noises and uncertainties in the annotations. However, advanced trackers with probabilistic regression are not studied sufficiently in the aspect of supervision and the aspect of robustness of evaluation maximization. In this paper, an overlap maximization network in the manner of probabilistic regression is proposed to improve the learning ability of the network and the discriminative ability in the evaluation maximization. Firstly, the probabilistic regression is extended with the intersection over union (IoU) evaluation, which is normalized as a probability density in the regression space. Secondly, the classification probability is added as a branch of the iterative evaluation module to improve the ability of distinguishing objects in the evaluation maximization. Moreover, the two branches are constructed into a joint probabilistic regression task of IoU evaluation, which makes the network learn from two types of ground truth and provide a consistent result with multi-branch outputs. For feature interpretation, the strip pooling network and the space-time memory network are introduced to encode long-range context and provide dynamic features, respectively. Compared to the state-of-the-art probabilistic regression trackers and other advanced trackers, the experiments show that the proposed tracker achieves outstanding performance across the six datasets, including GOT-10k, LaSOT, TrackingNet, UAV123, OTB-100 and VOT2018.