I am reading  some novel papers about long term object tracking in 2023.

Compare to  normal object tracking, the long term object tracking must have the ability to find the missing target object. The papers are as follows:

1. “MFT: Long-Term Tracking of Every Pixel”

The paper’s purpose:

Solving the challenges, such as dense,  pixel-level, long-term tracking.

Contributions:

(1) The approach exploits optical flows estimated not only between consecutive frames, but also for pairs of frames at logarithmically spaced intervals.

(2) It then selects the most reliable sequence of flows on the basis of estimates of its geometric accuracy and the probability of occlusion, both provided by a pre-trained CNN.

Personal Evaluation:

The paper exploits a new routine, which is worth reading.

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

The paper’s purpose:

Cmbining the capabilities of baseline trackers in the context of long-term visual object tracking.

Contributions:

(1) Proposing a strategy which can perceive whether the two trackers are following the target object through an online learned deep verification model to  select the best performing tracker as well as it corrects their performance when failing.

Personal Evaluation:

The long term tracker usually own the ability to switch the local tracker and global tracker.This paper may propose a novel way.

3. Target-Aware Tracking with Long-term Context Attention

The paper’s purpose:

(1) Unlike siamese tracker,  exploiting contextual information.

(2) Coping with large appearance changes, rapid target movement, and attraction from similar objects.

Contributions:

(1) Proposing a LCA (transformer ) which uses the target state from the previous frame to exclude the interference of similar objects and complex backgrounds.

Personal Evaluation:

A normal tracker with new transformer.

4. Multi-Template Temporal Siamese Network for Long-Term
Object Tracking

The paper’s purpose:

(1) Avoiding defects of  siamese tracker.

(2) Coping with target appearance changes and similar objects .

Contributions:

(1) Learning the path history by a bag of templates

(2) Projecting a potential future target location in a next frame.

Personal Evaluation: Competitor! Consensus of ideas.Worth reading.

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

The paper’s purpose:

(1) Improving siamese tracker.

(2) large variance, presence of distractors, fast motion, or target disappearing and the like .

Contributions:

(1) Exploiting cross-level Siamese features to learn robust
correlations between the target template and search regions.

(2) Proposing inference strategies to prevent tracking loss
and realize fast target re-localization.

Personal Evaluation: Normal. I want to know how to re-locate. Worth reading.

6. ‘Skimming-Perusal’ Tracking: A Framework for Real-Time and Robust Long-term Tracking

The paper’s purpose:

(1) Traditional long-term tracker are limited.

(2) To find the missing target object and accerlate the speed of global search.

Contributions:

(1) Determining whether the tracked object being present or absent, and then chooses the tracking strategies of local search or global search respectively in the next frame.

(2) Speeding up the image-wide global search,
a novel skimming module is designed to efficiently choose
the most possible regions from a large number of sliding
windows.

Personal Evaluation: The global search  is useful. Worth reading.