FeatureBasedTracking : Différence entre versions
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The goal of this page is to record the objectives of the development of a vision-based road user tracking tool at Ecole Polytechnique and Carleton University. | The goal of this page is to record the objectives of the development of a vision-based road user tracking tool at Ecole Polytechnique and Carleton University. | ||
| − | + | ==General Objectives== | |
# Feature-based tracking, using any "standard" feature tracker, eg KLT | # Feature-based tracking, using any "standard" feature tracker, eg KLT | ||
#* look at fast features and the opencv sample video_homography | #* look at fast features and the opencv sample video_homography | ||
| Ligne 9 : | Ligne 9 : | ||
# Semi-automated tracking | # Semi-automated tracking | ||
# Other vehicle/road user tracking methods, eg using background subtraction, tracking by detection (HoG detection)... | # Other vehicle/road user tracking methods, eg using background subtraction, tracking by detection (HoG detection)... | ||
| − | * Resources | + | |
| + | ==Summer 2011 project by Ananda Narayanan== | ||
| + | |||
| + | Expected list of features for the program: | ||
| + | |||
| + | * feature-based tracking using the KLT implementation of OpenCV, that can be easily extended to use other types of features (eg the fast features used in the opencv sample video_homography) | ||
| + | ** it should include at least the following constraints on motion regularity to avoid errors and improve results | ||
| + | *** features have a minimum length, minimum displacement over the last n frames, the angle between two successive displacement vectors and the ratio of their norm may not be too large (if not, the feature track is disrupted: if the feature is too short, it is discarded) | ||
| + | *** features in the same group should have a minimum temporal overlap, there is a minimum number of features in a group of moving objects + the connection and segmentation distances | ||
| + | * clean and modular design that can be easily extended: speed should be reasonable | ||
| + | ** in particular, the steps of feature tracking and grouping can be performed separately or together | ||
| + | * use of a homography to transform coordinates from image to world space | ||
| + | * clean interface using the command line and a configuration file | ||
| + | * saving all trajectory information for features and moving objects using the project https://bitbucket.org/trajectories/trajectorymanagementandanalysis (if possible, re-use data structure from the same project to avoid time-consuming type conversions) | ||
| + | |||
| + | If time allows: | ||
| + | |||
| + | * cross-platform compilation | ||
| + | * test of various thresholds and other constraints to improve tracking | ||
| + | * motion-compensation | ||
| + | * GUI for the correction of results | ||
| + | |||
| + | ==Resources== | ||
** http://code.google.com/p/opencv-feature-tracker/ | ** http://code.google.com/p/opencv-feature-tracker/ | ||
** https://bitbucket.org/Nicolas/trafficintelligence | ** https://bitbucket.org/Nicolas/trafficintelligence | ||
** https://bitbucket.org/trajectories/trajectorymanagementandanalysis | ** https://bitbucket.org/trajectories/trajectorymanagementandanalysis | ||
Version du 9 juin 2011 à 23:37
The goal of this page is to record the objectives of the development of a vision-based road user tracking tool at Ecole Polytechnique and Carleton University.
General Objectives
- Feature-based tracking, using any "standard" feature tracker, eg KLT
- look at fast features and the opencv sample video_homography
- Compensating small camera vibration (could work for small re-calibrations)
- an idea is to use features on background objects
- Generic features
- Semi-automated tracking
- Other vehicle/road user tracking methods, eg using background subtraction, tracking by detection (HoG detection)...
Summer 2011 project by Ananda Narayanan
Expected list of features for the program:
- feature-based tracking using the KLT implementation of OpenCV, that can be easily extended to use other types of features (eg the fast features used in the opencv sample video_homography)
- it should include at least the following constraints on motion regularity to avoid errors and improve results
- features have a minimum length, minimum displacement over the last n frames, the angle between two successive displacement vectors and the ratio of their norm may not be too large (if not, the feature track is disrupted: if the feature is too short, it is discarded)
- features in the same group should have a minimum temporal overlap, there is a minimum number of features in a group of moving objects + the connection and segmentation distances
- it should include at least the following constraints on motion regularity to avoid errors and improve results
- clean and modular design that can be easily extended: speed should be reasonable
- in particular, the steps of feature tracking and grouping can be performed separately or together
- use of a homography to transform coordinates from image to world space
- clean interface using the command line and a configuration file
- saving all trajectory information for features and moving objects using the project https://bitbucket.org/trajectories/trajectorymanagementandanalysis (if possible, re-use data structure from the same project to avoid time-consuming type conversions)
If time allows:
- cross-platform compilation
- test of various thresholds and other constraints to improve tracking
- motion-compensation
- GUI for the correction of results
