Let’s take a look at the summary first, it’s all well written
Multi-target tracking and full analysis, the most complete in the whole network (very good)
In engineering practice, why does target detection need to add target tracking?
Talking about Multi-target Tracking--Rapid Application Deployment
Discussion on the top algorithm of MOT
Anchor-free application overview: target detection, instance segmentation, multi-target tracking
Tracking Objects as Points
Github：GitHub - xingyizhou/CenterTrack: Simultaneous object detection and tracking using center points.
Based on CenterNet : How to evaluate the latest two point-based target detection models CenterNet?
CenterNet: Objects as Points paper study notes + code reproduction
Very well written: Intensive reading of the paper - CenterNet: Objects as Points
The code explanation is particularly good: centernet understanding + demo code analysis
CenterTrack : Paper Study Notes - CenterTrack
Interpretation of the source code of 3D target detection based on CenterTrack
Disadvantages are also advantages:
CenterTrack is purely local. It only associates objects in adjacent frames, with-out reinitializing lost long-range tracks. It trades the ability to reconnect long-range tracks for simplicity, speed, and high accuracy in the local regime.
A Simple Baseline for Multi-Object Tracking
Paper：https://arxiv.org/abs/2004.01888
Paper Reading-FairMOT: "A Simple Baseline for Multi-Object Tracking"
FairMOT understanding and implementation
FairMOT project code actual combat, modestly called bronze, is actually the king
CVPR 2020 multi-target tracking algorithm FairMOT code demo operation and training
Detection calculates the inter-class features of people, and distinguishes whether the detection part is a person or a background. Re-ID calculates the intra-class features of a person to distinguish who the person is.
ByteTrack
Github：
https://github.com/ifzhang/ByteTrack
Compare
Target Tracking (2) SDE, JDE, FairMot, CenterTrack, Bytetrack Tracking Comparison