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MOT using deepsort and yolov3 with pytorch

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for track in self.tracks: if not track.is_confirmed(): continue features += track.features targets += [track.track_id for _ in track.features] track.features = [] 在第二帧中,len(features)和len(targets)都为0,到了第三帧就都为84,且里面的数据是有3份相同的。为什么会出现3份相同的数据呢?

I have tried all possible ways for compiling NMS module. Nothing works! Please help me with this. GCC version- 7.5.0 torch - 1.11.0 torchvision - 0.12.0 python 3.7.16 As per...

你好,我把reid模型转成onnx模型后,用onnxruntime推理,处理同一张图片,前处理是一样的; torch的模型输出,前15位: [0.01681411 0.05290637 0.03931383 0.02069167 0.05464995 0.05208057 0.03364787 0.02164895 0.04400286 0.04631684 0.00424759 0.04806569 0.03491891 0.04207694 0.00969397] onnx的模型输出前15位: [1.3163207e-02 3.3286333e-02 3.0172531e-02 2.7824650e-05 5.4625902e-02 2.5197309e-02 4.4952868e-03 5.4582981e-03 2.5899781e-02 3.7061449e-02 1.8202272e-03 1.9214328e-02...

https://github.com/ZQPei/deep_sort_pytorch/blob/8cfe2467a4b1f4421ebf9fcbc157921144ffe7cf/deep_sort/deep/model.py#L56

Bumps [joblib](https://github.com/joblib/joblib) from 0.14.1 to 1.2.0. Changelog Sourced from joblib's changelog. Release 1.2.0 Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported....

dependencies

add some contents about YOLOv5 detector, tracking results showing and modify some errors about kalman_filter.py, detector.YOLOv3.weight.yolo_utils.py(This eliminates the need for compiling NMS operators).

当目标离开镜头再回到镜头,或者发生遮挡object ID会发生变化。 When the target leaves the camera and then returns to the camera, or when occlusion occurs, the object ID will change.

Fixed question about `deep_sort/deep/resnet.py` mentioned in the [issue(#276)](https://github.com/ZQPei/deep_sort_pytorch/issues/276).

During using it, I found that the performance of using ResNet was not better than the network used in the original paper. Finally, I found that the problem comes from...