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@ManuelPalermo ManuelPalermo commented Apr 2, 2021

Hi, tried improving detection consistency across frames using BBox tracking with the SORT algorithm (https://arxiv.org/pdf/1602.00763.pdf). The implementation was completely ported from the original author (https://github.com/abewley/sort)

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mdraw commented Apr 6, 2021

Thanks for your work @ManuelPalermo, that sounds like a great feature! I'm not sure if everything works as it should with the given default configuration though.
I've tested your implementation on some rather challenging video clips and found that with the default settings (--track-config '{}') there were actually more missing face detections. For example using --track-config '{"max_age": 100, "min_hits": 0, "iou_threshold": 0.01}' instead, the results were similar to not using the tracking feature at all. Can you recommend a good default setting with which the results are improved?
In addition, can you share a video clip where you see an improvement by using the new tracking feature so I can test it?

dets, _ = centerface(frame, threshold=threshold)

if tracking_kwargs is not None:
dets = tracker.update(dets)
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If you just replace dets with the tracker.update() result, the 5th entry of each detection array, which originally contains the detection score, is replaced with the (integer) object ID (https://github.com/ORB-HD/deface/pull/11/files#diff-3842ed04dbf395f7ad3f223e01881b86518e46e46fa6bd80e60467d970bdf5a4R205). The score should be kept so it can be visualized later with --draw-scores.

--track-config SORT arguments for bounding-box detection tracking, or
None if tracking should not be used.
This argument is expected in JSON notation. For a list
of possible options, refer to the original SORT repository.
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SORT is quite hard to discover through Google, so I'd rather link directly to its options or describe them, maybe based on the help texts provided here: https://github.com/abewley/sort/blob/master/sort.py#L261-L267

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