HAL interface for Sharr derivatives needed for Lukas-Kanade algorithm #26163
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Added HAL interface for Lukas-Kanade optical flow #26143
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video: fix vittrack in the case where crop size grows until out-of-memory when the input is black #25771
Fixes https://github.com/opencv/opencv/issues/25760
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Make \epsilon parameter accessible in VariationalRefinement #24852Resolves#24847
I believe this is necessary to expose \epsilon parameter.
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Video tracking (dnn): set backend and target for TrackerVit #24461Resolves#24460
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VIT track(gsoc realtime object tracking model) #24201
Vit tracker(vision transformer tracker) is a much better model for real-time object tracking. Vit tracker can achieve speeds exceeding nanotrack by 20% in single-threaded mode with ARM chip, and the advantage becomes even more pronounced in multi-threaded mode. In addition, on the dataset, vit tracker demonstrates better performance compared to nanotrack. Moreover, vit trackerprovides confidence values during the tracking process, which can be used to determine if the tracking is currently lost.
opencv_zoo: https://github.com/opencv/opencv_zoo/pull/194
opencv_extra: [https://github.com/opencv/opencv_extra/pull/1088](https://github.com/opencv/opencv_extra/pull/1088)
# Performance comparison is as follows:
NOTE: The speed below is tested by **onnxruntime** because opencv has poor support for the transformer architecture for now.
ONNX speed test on ARM platform(apple M2)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| nanotrack| 5.25| 4.86| 4.72| 4.49|
| vit tracker| 4.18| 2.41| 1.97| **1.46 (3X)**|
ONNX speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| nanotrack|3.20|2.75|2.46|2.55|
| vit tracker|3.84|2.37|2.10|2.01|
opencv speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| vit tracker|31.3|31.4|31.4|31.4|
preformance test on lasot dataset(AUC is the most important data. Higher AUC means better tracker):
|LASOT | AUC| P| Pnorm|
|--------|--------|--------|--------|
| nanotrack| 46.8| 45.0| 43.3|
| vit tracker| 48.6| 44.8| 54.7|
[https://youtu.be/MJiPnu1ZQRI](https://youtu.be/MJiPnu1ZQRI)
In target tracking tasks, the score is an important indicator that can indicate whether the current target is lost. In the video, vit tracker can track the target and display the current score in the upper left corner of the video. When the target is lost, the score drops significantly. While nanotrack will only return 0.9 score in any situation, so that we cannot determine whether the target is lost.
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[teset data in opencv_extra](https://github.com/opencv/opencv_extra/pull/1016)
NanoTrack is an extremely lightweight and fast object-tracking model.
The total size is **1.1 MB**.
And the FPS on M1 chip is **150**, on Raspberry Pi 4 is about **30**. (Float32 CPU only)
With this model, many users can run object tracking on the edge device.
The author of NanoTrack is @HonglinChu.
The original repo is https://github.com/HonglinChu/NanoTrack.
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If there will be measurement before the next predict, `statePost` would be assigned to updated value. So I guess these steps are meant to handle when no measurement and KF only do the predict step.
```cpp
statePre.copyTo(statePost);
errorCovPre.copyTo(errorCovPost);
```
Fix unsigned int bug in computeECC
* address issue with unsigned ints in computeEcc
* remove additional logic checking firstOctave
* use swap instead of same src/dst
* simplify the unsigned check logic
* Fixed OCL implementation of pyrlk
If prevPts size is (N, 1) (which is a default layout for converting `vector<Point2f>` to `UMat`) the `prevPts.cols == 1` and optical flow will be calculated for the first point only.
Getting `prevPts.total()` as in line 1048 is the correct way to get points count.
* fixed compilation warning (size_t to int)
Signed-off-by: Sergey Krivohatskiy <s.krivohatskiy@gmail.com>
* Convert lkpyramid from SSE SIMD to HAL - 90% faster on Power (VSX).
* Replace stores with reduce_sum. Rework to handle endianess correctly.
* Fix compiler warnings by casting values explicitly to shorts
* Switch to CV_SIMD128 compiler definition. Unroll loop to 8 elements since we've already loaded the data.
