State of the art object tracking with neural networks

Anyone following the latest models for object tracking in computer vision?

Object tracking consists of identifying an object (or bounding box of the object) on every single frame of a video. It usually combines object detection with motion prediction to handle occlusion and other subtle issues that cannot be solved with object detection alone.

I found a couple of models like Detectron and Detectron2 by Facebook research, but it would be nice to get additional resources from people working actively in this field.

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After next 10th, I am also interested in this for JuliaImages and all downstream use cases that can serve many fellow organisations.

Might be of interest(mostly classical though)

@yakir12 is also interested in this for his work.


I believe that liquid neural networks are the SOTA currently for this. I also think it’s one of the best approaches for this currently.

This looks at navigation but have in earlier papers focused on realtime object tracking.

Just my two cents.


The Liquid Neural Networks are the best for it as they have the distinctive architecture that holds potential advantages for various computer vision tasks. LNNs are specialized for handling temporal data with long-range dependencies and complex patterns. They would excel in scenarios where the visual dynamics are intricate, evolving over longer time intervals, or where understanding complex interactions is essential. RNNs, on the other hand, are versatile for a wide range of sequential data tasks and may be more suitable for simpler temporal relationships in visual data.
So LLNs are a better choice…