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.