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Hi, @yeongnamtan! 👋🏻 Correct me if I'm wrong, but DeepSort requires loading the model in PyTorch or TensorFlow. We are trying to make Supervision not require installation of such heavy dependencies.
@SkalskiP I did a comparison between ByteTrack (using LINE COUNTER), and DeepSort for the same source video. DeepSort performed much better in terms of counting.
For ByteTrack, my settings as follow:
byte_tracker = sv.ByteTrack(track_thresh=0.3, track_buffer=60, match_thresh=0.9, frame_rate=30)
For DeepSort, confidence level same at 30%
DEEPSORT:
MODEL_TYPE: "osnet_x_25"
MAX_DIST: 0.1 # The matching threshold. Samples with larger distance are considered an invalid match
MAX_IOU_DISTANCE: 0.7 # Gating threshold. Associations with cost larger than this value are disregarded.
MAX_AGE: 30 # Maximum number of missed misses before a track is deleted
N_INIT: 3 # Number of frames that a track remains in initialization phase
NN_BUDGET: 100 # Maximum size of the appearance descriptors gallery
Hi, @yeongnamtan! 👋🏻 Correct me if I'm wrong, but DeepSort requires loading the model in PyTorch or TensorFlow. We are trying to make Supervision not require installation of such heavy dependencies.
DeepSORT has no such requirements. It only requires the bounding box detection and a feature vector. The feature vector can be generated via a secondary model or extracting a specific layer of another model. It doesn't matter if it's TF, PyTorch or whatever.
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Description
Would you consider including DEEP SORT Tracker in Supervision ?
Use case
No response
Additional
No response
Are you willing to submit a PR?
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