週報(SUN YUYA)

Contine reading papers about long term tracking.

  1. Robust Long-Term Object Tracking via Improved Discriminative Model Prediction

The paper try to modify the superdimp to a long-term tracker. It present a global search method and

(1) Baseline tracker using random erasing.

Method: Erase a random small rectangular areas of image to confirm whether the prediction is reliable.

Evaluation:I hope it  works.

(2) Global search using random searching.

Method: First, we create global searching templates with a predetermined interval. Next, we adaptively determine the number of searches according to the ratio of the image size to the target size. Then, an object is detected within a randomly selected searching area.

(3) Score penalty.

However, the probability of an object disappearing and suddenly appearing at a distant location is very low. To prevent this sudden detection, we penalize a confidence score through spatio-temporal constraints, which is expressed as follows:

周报

已完成的任务

  • 成功和苏州通达机器物流有限公司接洽,帮助工厂从半自动化向全自动化转型。预计项目规模300万;
  • 前往苏州大学新一代网络创新实验室进行有关深度学习的主题汇报;
  • 收到清华大学苏州汽车研究院的邀请,我的论文模型可以在他们数据集上进行测试。如果结果良好,将开展合作。

回日本(5.9)前的任务

  • 完成苏州通达机器物流有限公司的企业需求文档
  • 移植模型到清华大学自己的无人驾驶车上进行测试
  • 完成基于我的新idea的论文