先輩とお話ししました。

内定者懇親会のためゼミは欠席します。

1.learn the framework of yolo-v8

2. learn the seam attention mechanism（https://arxiv.org/abs/2208.02019）

3. Try to come up with a better data fusion framework

4.Evaluation mechanisms are learning

面接があるので今週のゼミはお休みします。

The details of some long-term trackers.

1 . SiamX: An Efficient Long-term Tracker Using Cross-level Feature Correlation and Adaptive Tracking Scheme.

The key is “ADAPTIVE TRACKING SCHEME”.

（1）Momentum Compensation.

Exploit the concept “fast motion” to judge whether the target object is lost.

“If the target displacements between consecutive frames exceeds target sizes, it considers the target object is at a fast-moving state. To avoid targets leaving the search regions, the search center drifts in the direction of momentum:”

conclusion： Fake paper. Its codes lack the long-term tracker.

2. Combining complementary trackers for enhanced long-term visual object tracking.

Running two trackers.

But we can use its score’s method to re-detect.

3. GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences

if  s1(f∗, x1) > s1(f∗, x2) and s2(f∗, x1) ≤ s2(f∗, x2) :

re-detect else: continue.

Key: motion residual. The key is “UHP-SOT”

4. High-Performance Long-Term Tracking with Meta-Updater

(1) appearance model (lstm)

(2) re-detection( the flag of DiMP ? )

Conclusion: Another fake paper. The most important point is DIMP !

5. UHP-SOT: An Unsupervised High-Performance Single Object Tracker(2017)

Methods: It has three trackers:

(1) Trajectories-based box prediction ( principal component analysis)

(2) Background motion modeling ( optical flow)

(3) Appearance model (normal tracker)

6. Object Tracking Using Background Subtraction and Motion Estimation in MPEG Videos (2005)

Key: Using four corner to compute the motion of background(Optical flow).

7. Fast Object Tracking Using Adaptive Block Matching(2005)

Key: Exploiting ‘Mode filter’ in order to straighten up noisy vectors (Optical flow) and thus eliminate this problem.

1. Finish paper "Corn plant phenotypic parameters extraction algorithm based on keypoint detection and stereo images"
2. Change my paper "FCOS_Stereo"

The long term object tracker requires the tracker to be able to retrieve lost targets. So I want to predict the possible locations where the target might appear based on the historical motion trajectory of the object.  Trajectory prediction requires the camera motion and current object tracking method can’t provide camera information,such as camera pose or motion.

In order to get depth map and camera poses, I am reading papers about slam with monocula camera, involving unsupervised learning.

1. Future Person Localization in First-Person Videos

Purpose:  predicting future locations of people observed in first-person videos.

key point :a)  ego-motion  b) Scales of the target person. c) KCF for tracking d) feature concatenating.

evaluation: Excellent introductory work. But how to get ego-motion information?

2. Unsupervised Learning of Depth and Ego-Motion from Video

Purpose: Presenting an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences.

Key point: a) Visual synthesis b)  unsupervised learning.

Evaluation: Nice paper. But it still need camera intrinsics.

3. Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras

Purpose: Presenting a novel method for simultaneously learning depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as a supervision signal.

Key opint: a) Generating camera intrinsics.

Evaluation: Nice paper. Rrovide code. But it may be too slow.

SOT Summary

The work summary for the last week and this week is as follows: Reviewed the progress and main contributions of single object tracking algorithms over the years (2010~2023), summarizing the respective advantages of several representative algorithms. In conjunction with single object tracking, I explored the development history of 2D to 3D detection and tracking.

I learned how to use Inkscape to draw image with format of .svg and output results without distortion.