Comments (6)
It depends. Do you know what is the slowest in your case? May be it's the local tracker (KCF, MIL etc). If you set tracking::TrackNone instead tracking::TrackKCF?
And some about GPU: in this project it used only OpenCV library without CUDA. But it can use built-in OpenCL version of algorithms on CPU, integrated GPU or discrete GPU.
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Since I change the detector to yolov3 and run the code on GPU,the whole process takes about 50ms/frame in one thread.
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You're right,when I set tracking::TrackNone instead of tracking::TrackKCF,the time costs less.Maybe do the detection and tracking in two threads will help too?
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Yes, it's a good idea.
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Waiting this Pull request: #78
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Can you update master branch and test speed?
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Related Issues (20)
- Tracking without detection HOT 1
- There are some errors when building the project. HOT 7
- Hello, why does the tracker code have a dat and graph part? Looks like too much complicated, HOT 1
- Compile python module under windows HOT 1
- YOLOv7 instance segmentation HOT 1
- File "video_with_traffic.mp4" is absent HOT 3
- Problems running tracker with DeepSORT HOT 6
- Point tracking, exe execution bug! HOT 2
- Mismatch pymtracking lib and Discuss about App Example 1 and Example 6 HOT 6
- Discuss about App Example 1 and Example 6 do not working with moving camera video? HOT 2
- The source code do not working with weight from yolov5 trained on custom dataset HOT 1
- Try to update GTL and MWBM versions
- Update Darknet sources
- Tracking with a real timestamps
- Bug with github actions
- About code migration HOT 1
- Maximum active tracks HOT 1
- m_boxMask how to use? and RotatedRect object how to track? HOT 1
- some tracked objects flying? HOT 3
- How could I use custom detector ? HOT 2
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