[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.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
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.
New computeECC function, and updated findTransformECC function to make gaussian filtering optional (#13837)
* fix for https://github.com/opencv/opencv/issues/12432 with doc and tests
* Added doc string for new parameter.
* Fixes suggested by Alalek for getting around ABI incompatibility.
* Update to docstring, to remove parameter that isn't relevant.
* More updates based on Alalek's usggestions.
- Before this PR, following tests failed on some platform.
CUDA_OptFlow/FarnebackOpticalFlow.Accuracy/19
CUDA_OptFlow/FarnebackOpticalFlow.Accuracy/23
- The algorithm now recognizes the OPTFLOW_USE_INITIAL_FLOW flag.
Previously, when the flag was set, it did not use the flow data
passed as input, instead used some garbage data in memory.
- More strict test limit.
