---
author:
- 'Jinlong Peng$^{1*}$'
- 'Zhengkai Jiang$^{1*}$'
- 'Yueyang Gu$^{1}$[^1]'
- 'Yang Wu$^{2}$[^2]'
- |
    \
    Yabiao Wang$^1$
- Ying Tai$^1$
- |
    Chengjie Wang$^1$Weiyao Lin$^3$ $^1$Tencent Youtu Lab\
    $^2$Kyoto University\
    $^3$Shanghai Jiao Tong University {jeromepeng, zhengkjiang, yueyanggu, caseywang, yingtai, gary71@example.com, gary71@example.com, gary71@example.com
title: 'SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking (Supplementary Material)'
---

Overview
========

This supplementary material includes:

-   -   The details of data augmentation in training.

-   -   The other SOTA methods, including SiamRPN++ and SiamFC++.

Comparison of Localization Prediction
=====================================

![ of localization-aware anchor-free Head. It consists of classification branch (above), localization branch (middle) and regression branch (below). <span data-label="fig:head"></span>](fig/head.pdf){width="\columnwidth"}

Details of Data Augumentation
=============================

The image pairs selected from the same video sequence are regarded as positive samples. The image pairs selected from the static images and different video sequences are regarded as negative samples. We set the ratio of positive negative samples 3:1. In addition, we random translation and images to avoid putting a strong center bias on objects.

![The localization comparison localization branch centerness. For centerness, center location has larger value, which is not suitable for most of ground-truth boxes. Our proposed target is the IoU between the regressed box and the matched ground-truth box, which dynamically changes to match localization accuracy of regressed boxes during training.<span data-label="centerness-vs-iou"></span>](./fig/centerness-vs-iou.pdf){width="0.95\columnwidth"}

Experiments on OTB-2015 and VOT-2018
====================================

#### OTB-2015.

OTB-2015 contains 98 sequences and 100 objects. In a quarter sequences of OTB-2015, frames are gray-scale images. Generally, Succ. and Prec. are used as metrics in OTB-2015. Table \[fig:otb\] presents the performance of evaluated trackers. Succ. score is the more reasonable metric than Prec. score. since it is normalized by the scale of boxes. Our SiamRCR obtains the best Succ. score,

#### VOT-2018.

We report experimental results on VOT-2018 in Table \[fig:vot2018\]. We can find that Ocean achieves Top-1 EAO and Top-2 Robustness score on VOT-2018. Our SiamRCR obtains Top-2 EAO and Top-2 Accuracy score on VOT-2018.

               Succ.$\uparrow$   Prec.$\uparrow$
  ----------- ----------------- -----------------
    SiamFC          0.582             0.711
     MDNet          0.678             0.909
    GradNet         0.639             0.861
     ATOM           0.667             0.879
    DiMP50          0.686             0.899
   SiamRPN++        ****              ****
     Ocean          0.672             0.902
   SiamFC++         0.683             0.896
    PG-Net          0.691             0.892
    SiamBAN         ****              ****
                    ****              0.900

  : Comparison of tracking results on OTB-2015 benchmark.<span data-label="fig:otb"></span>

Quantitative Results
====================

The quantitative results of our proposed SiamRCR on GOT-10k dataset are . We also present the quantitative results of two representative trackers: anchor-based SiamRPN++ and anchor-free SiamFC++ comparison. three trackers are initialized with same ground-truth in the first frame which is shown in green in Figure \[fig:quantitative\].

that SiamRPN++ and SiamFC++ may fail to targets in cases of fast motion, scale variation and occlusion. In sequence 42, SiamRPN++ and SiamFC++ drift from the moving animal in frame 38. Our proposed can locate with more localization confidence . In 77 and 104, there are distractors targets in the scene. In sequence 82, SiamRCR scale of the flying .

![image](fig/quantitative.pdf){width="\textwidth"}

[^1]: *Equal contribution.*

[^2]: *Corresponding author: Yang Wu (gary71@example.com).*

{'source': 'arXiv_out/2105'}