説明変数をランダムに選んでモデルを作成するコードを作成した。
データを大量に増やした。
週報(Wen Tian)
I ended up on a New Year's vacation for almost a month. It's been a great time. At the same time, research can't be slack.
I will continue to work on putting 3D geometry constraints and how to optimize depth maps.
週報(SUN YUYA)
（1）Writing the paperof ICIAE2024.
( 2 ) In the experiments about long term tracking, I found that the similarity between templates and current appearance is not reliable. Because what we need is the function that can identify whether two images are the same object, rather than the similarity distance between two images.
During tracking, the same object can have different appearance. The similarity distance between 0 and 1 are not suitable for judge.
So we should find another way in the field of image classification.
週報(SUN YUYA)
The experiements of re-detection with confidence score about long term tracking.
The confidence score is the biggest score to select the best location in each frame, which can be the basis of judge whether the target is lost. We can use the single confidence score or the sequenced confidence score to evaluate the tracking process.
Mixformer is a excellent short term trackers and Unicorn is a excellent glocal tracker. Our purpose aims to modify Mixformer to a long object trackers. So we want to add a re-detection mechanism. When the mechanism judge the tracking is failed, the running tracker will exchange to the global tracker to find the lost target object.
In this experiments, the re-detection mechanism exploit the confidence score to judge whether the target is lost.
Lasot is the benchmark of long term object tracking and average confidence score of mixformer in the lasot is 0.58 when IOU is zero.
The experiments are as follows:
lasot | Success | Precision | Norm Precision | Mixformer-base | 0.711 | 0.757 |0.740 | M_U_sc_30 | 0.713 | 0.761 |0.743 | M_U_sc_58 | 0.711 | 0.758 |0.742 | M_U_sc_sq_10_30 | 0.718 | 0.764 |0.749 | M_U_sc_sq_20_30 | 0.719 | 0.766 |0.749 | M_U_sc_sq_50_30 | 0.718 | 0.765 |0.747 | M_U_sc_sq_100_30 | 0.715 | 0.761 |0.745 | M_U_sc_sq_200_30 | 0.714 | 0.760 |0.743 | M_U_sc_psq_10_30 | 0.718 | 0.764 |0.748 | M_U_sc_psq_20_30 | 0.718 | 0.765 |0.748 | M_U_sc_sq_10_58 | 0.715 | 0.762 |0.745 | M_U_sc_sq_200_58 | 0.715 | 0.763 |0.745 |
Where M_U_sc_30 is the tracker using score under 0.3 and M_U_sc_58 is the tracker using score under 0.58. M_U_sc_sq_10_30 means that average ten confidence scores under 0.3. And Psq uses penalty.
The 0.3 may be more effective than 0.58. The best length of sequence may be 20. The penaly seems not effective. It’s too cumbersome to conduct more experiments on hyperparameters
In summary, we can see that exploiting confidence score to re-detect works but is not obvious. So in the next step, we can conduct experiemts on the similarity of templates and tracking object.
週報(白川)＃他人のデータ＃スマートウォッチ＃結論
研究進捗
- 他人のデータを自分のモデルに突っ込んでみた（自分のデータに混ぜて標準化、別々で標準化2パターン）
→わかってはいたがうまく判定できなかった。
良モデル作成には、今のところ自分のデータを使うしかない（飲酒前後30分でできる）
- スマートウォッチを使って取得した自身のデータを使って飲酒状態を推定した
→少ないデータで学習し構築したモデルの方が精度が良かった。
学習データ少↓
学習データ多↓
週報(西元)
実際に教室(5-2A)でどのくらいの距離まで認証されるのか試してみた。
週報(野崎)
中心窩画像処理のプログラミングを完成しました。中間領域の解像度を変換する距離を偏心度視力モデルの図から決めていこうと思います。
週報(白川)
本日他人のデータを撮ってモデルの評価をしたいと思います。
週報(GAO)
1.update my flow
2.As what i do in the last work,using internal parameters to project the key point to make data.
3.Adding the stereo -> depth netwotk to obtain the depth map
週報(TANG)
Reading papers