Actually, DeepSORT doesn't use a hypothesis tree, which is common in many hyped CV tracking algorithms. I believe this is due to the abundant contextual data available in dense images. However, in self-driving scenarios, where other sensors are involved and the data (like sparse point clouds) is more challenging, these algorithms might not be as effective. The tracking requirements in such cases, often dealing with small and sparsely distributed data and frequent missed detections, necessitate improved and different tracking approaches.
Yes, I am familiar with it. Thank you for mentioning it. It's really cool that in the comments we have a lot of options for similar projects.
As mentioned earlier with Stone Soup, there is an interesting point. Most tracking libraries were developed by labs connected with the military and were primarily in MATLAB, due to the long history of the topic. Now, with the growth of autonomous vehicles and robotics, interest in this topic is returning.
Yes, you're right about Stone Soup. It's a great library with a wide range of code for developing tracking algorithms. And it's in Python. I really like this library!
But my approach is a bit different. I'm focusing on creating algorithms in a single file and interactive notebooks. This should help those who want to learn the topic and understand how the algorithms work, rather than just the library interface. After learning, these algorithms can be applied in any library, whether it's Stone Soup or MATLAB.
Stone Soup seems to be trying to be quite comprehensive about algorithms, and composable in a way that your library isn't, so it's perhaps not ideal for learning. But then it's pure Python, so probably not useful for live use in most production problems (assuming a fairly large number of targets). It's a bit awkward - it ends up bit really meeting either need. I think it would be good for experimenting with a new tracking algorithm though.
I think targetting your library firmly at learning puts it at an advantage for that.
Yes, I agree that Stone Soup is well-suited for research because it allows for extensive customization of the scenario and algorithm. However, due to its Python base, it's not the best solution for production environments.
Thank you, I'll continue working in this direction.
