KLTKanadeLucasTomasi feature tracking algorithm in embedded hardware

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KLTKanadeLucasTomasi feature tracking algorithm in embedded hardware

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KLT(Kanade-Lucas-Tomasi) feature tracking algorithm in embedded hardware. Yamini Balasukumaran. Rekha Dilli Babu. Project Goal. Track mice using embedded hardware ... – PowerPoint PPT presentation

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Title: KLTKanadeLucasTomasi feature tracking algorithm in embedded hardware

1
KLT(Kanade-Lucas-Tomasi) feature tracking
algorithm in embedded hardware

Yamini Balasukumaran
Rekha Dilli Babu

2
Project Goal

Track mice using embedded hardware
Implement KLT algorithm for tracking
Efficient embedded system implementation

3
Project Plan

Study the theory for KLT (Good features to
track).
Study the given KLT implementation details(Vision
group at Stanford University)
Familiarize with the TMS320C5510 DSP kit.
Implement the KLT in TMS320 to track mice.

4
KLT Algorithm TheoryGood features to track

Feature selection deals with extracting
attributes that result in some quantitative
information of interest.
Tracker does not track single pixels but windows
of pixels.
Good window is one that can be tracked well.
The goodness of each pixel is measured as the
minimum eigenvalue of the 2 by 2 gradient matrix
computed from the tc-gtwindow_width by
tc-gtwindow_height window around the pixel
Eigen values indicate whether there is a constant
intensity profile, unidirectional pattern or salt
and pepper textures.

5
KLT Algorithm Implementation

Functions Has functions to select good features,
track the selected features from one frame to
another(KLTSelectGoodFeatures,KLTTrackFeatures).
Can customize the tracker by manually setting
various parameters like mindist, Window_width,
window_height, min_eigenvalue, subsampling.
Structures KLT_TrackingContext (to customize
tracker), KLT_Feature, KLT_FeatureList.

6
KLT Implementation Details

KLT_Feature
typedef struct
KLT_locType x
KLT_locType y
int val
KLT_Feature

KLT_TrackingContext
typedef struct
int mindist
int window_width, window_height
KLT_BOOL sequentialMode
KLT_BOOL smoothBeforeSelecting
KLT_BOOL writeInternalImages
int min_eigenvalue
float min_determinant
float min_displacement
int max_iterations
float max_residue
void pyramid_last_grady
KLT_TrackingContext

7
KLT Implementation DetailsKLTSelectGoodFeatures

Gradients are computed in x and y direction and
are used to select the features
tc-skippedPixels can be used to speed up the
process
Goodness of pixel measured as minimum Eigen value
of gradient matrix
Pixels are sorted in ascending order of goodness
nFeatures are selected
Meet mindist and min_eigen value.

KLTSelectGoodFeatures()
void KLTSelectGoodFeatures(
KLT_TrackingContext tc,
KLT_PixelType img,
int ncols,
int nrows,
KLT_FeatureList fl)

8
Hardware - TMS320C5510 DSK kit

Chosen because of availability with the idea
that the design could be ported to another
appropriate hardware.
TMS320C5510 DSK kit has fixed point DSP
processor(200 Mhz ), has support for DMA.
Supports Multiply accumulate hardware(MAC)
instruction - intrinsic functions
sum
(ppp) (kernel.datak)
sum _smac(sum,ppp,kernel.datak)
On chip hardware timer .

9
Implementation of Feature selection(Embedded
system)

Measured the execution time of the
KLTSelectFeatures function using the high
resolution timer functions.
Start CLK_gethtime()
functioncall()
stop CLK_gethtime()
time_used stop - start
num_clocks_count
CLOCK_FREQ_MS/CLK_countspms()
num_clocks_used
num_clocks_count time_used
tottime (num_clocks_used/200000)
In the case of 45 by 22 frame image this
value was 51ms for the KLTSelectFeatures
function.

10
Implementation of Feature selection(Embedded
system)

File-IO execution time (3 usec) was found to be
negligible compared to the Select-features
function(51 ms).
The periodic function scheduled for 70 ms. (14
frames per sec)
Separate the signal processing functions from the
file formatting functions. If implemented on a
system like C5471 which has an ARM and DSP then
it would be easier to implement.

Periodic-function Generator (every 70 ms)
Image-file Pre-processor
KLt-Feature-Selection
11
Results

Location X1 Y1 X2 Y2
KLT reference implementation 22 13 31 13
Embedded implementation 22 12 29 12

12
Tracking mice
13
Scope for improvement

Choose a Floating point processor.
Set-up which has streaming data interface
Parallel processing to improve speed up (e.g..
for computing horizontal and vertical gradients)
Change the algorithm to save on memory
allocations.

14
Conclusion

Implemented the feature selection portion of the
algorithm with limited scope.

15
References

http//vision.stanford.edu/birch/klt/
Jianbo Shi and Carlo Tomasi. Good Features to
Track. IEEE Conference on Computer Vision and
Pattern Recognition, pages 593-600, 1994.
TMS320C5510 Reference Manuals

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