Exemplar Loss for Siamese Network in Visual Tracking

TL;DR

This paper introduces an exemplar loss for Siamese networks in visual tracking that improves discrimination by reducing inner products among exemplars, leading to better performance.

Contribution

The paper proposes a novel exemplar loss integrated with logistic loss to enhance feature discrimination in Siamese tracking models.

Findings

Outperforms logistic and triplet loss supervised methods

Achieves comparable results on public benchmarks

Enhances feature discrimination among exemplars

Abstract

Visual tracking plays an important role in perception system, which is a crucial part of intelligent transportation. Recently, Siamese network is a hot topic for visual tracking to estimate moving targets' trajectory, due to its superior accuracy and simple framework. In general, Siamese tracking algorithms, supervised by logistic loss and triplet loss, increase the value of inner product between exemplar template and positive sample while reduce the value of inner product with background sample. However, the distractors from different exemplars are not considered by mentioned loss functions, which limit the feature models' discrimination. In this paper, a new exemplar loss integrated with logistic loss is proposed to enhance the feature model's discrimination by reducing inner products among exemplars. Without the bells and whistles, the proposed algorithm outperforms the methods supervised by logistic loss or triplet loss. Numerical results suggest that the newly developed algorithm achieves comparable performance in public benchmarks.