Nicolai Wojke

I could not yet reproduce the problem. I'll leave the issue open for now.

How long is your sequence, how large your optimization window?

You must be running it for quite a while, I guess the issue then really is constructing the trajectories not solving the min-cost flow problem. Anyways, i'll be looking into...

I have adapted the mcf library to allow for this use case scenario in branch [feature/batch_processing_update](https://github.com/nwojke/mcf/tree/feature/batch_processing_update). I haven't found the time to update the mcf-tracker to use this API other...

It returns a dictionary. The track id is the key, the sequence of nodes in the graph/solver that are on the trajectory is the value. ``` for track_id, trajectory in...

Ah, yes. I had only tested with the C++ interface so far. These node attributes need to be pruned as well. There is a `num_pruned_locations_` in the BatchProcessor class. All...

I finally found some time to work on this, although not as much as I wanted. In branch [feature/mcf_update](https://github.com/nwojke/mcf-tracker/tree/feature/mcf_update) the MinCostFlowTracker class has been changed to return a dictionary that...

In line 5 the loop goes over all ``n=1 ... max_age``. Inside the loop, line 6 matches measurements against all tracks with age ``n``. Lines 8 and 9 remove the...

Sorry for the late reply. The threshold is [here](https://github.com/nwojke/deep_sort/blob/master/deep_sort/linear_assignment.py#L182). Instead of changing the threshold you could also increase the motion model uncertainty of the Kalman filter [here](https://github.com/nwojke/deep_sort/blob/master/deep_sort/kalman_filter.py#L52).

Sorry for the late reply. S is the covariance of the (predicted) track state, projected into measurement space. It is denoted as S_k on [Wikipedia](https://en.wikipedia.org/wiki/Kalman_filter#Update).