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Detection of Temporary Objects in Mobile
Lidar Point Clouds
Xinwei Fang
Guorui Li
Kourosh Khoshelham
Sander Oude Elberink
BACKGROUND
 Mobile laser scanning provides an accurate recording of road
environments useful for many applications;
 Usu...
APPROACH
Static
 Temporary objects:
Moving
 Static temporary objects:
 segmentation + classification based on shape fea...
STATIC TEMPORARY OBJECTS
 Classification based on shape features:
Linear
& short
Linear
& tall
Volumetric
& tall
Planar V...
MOVING TEMPORARY OBJECTS
 Detection based on closest point analysis with two
sensor data
Sensor 1
Sensor 2
5
METHODS
1. Ground removal
2. Connected-component segmentation
3. Static temporary objects:
 feature extraction + classifi...
GROUND REMOVAL
 Plane-based segmentation
 Ground = large & low segment
7
CONNECTED COMPONENT SEGMENTATION
 Points that are closer than a certain distance (e.g. 30 cm) belong
to one connected com...
FEATURE EXTRACTION
 Shape features
Size (number of points)
Area on horizontal plane
Mean density
Height of the lowest...
CLASSIFICATION
 Ground truth for training and evaluation: manual labeling (115 samples)
 Feature selection
 Forward Sel...
CLOSEST POINT ANALYSIS
Static Moving
Static
objects
Moving
objects
Number 45 24
11
EXPERIMENTS
 Study area
Enschede, urban
 Data
TopScan MLS
One strip: ~20 mio points
12
RESULTS
Classifier Completeness Correctness
LDC - all features 0.90 0.86
LDC - FS (8 features) 0.90 0.79
LDC - BE (8 featu...
RESULTS
 Classification results for the whole strip :
14
RESULTS
Sensor 1 Sensor 2 Sensor1 + Sensor2
Completeness 0.93 0.86 0.90
Correctness 0.90 0.96 0.93
Overall Accuracy 0.84 0...
LIMITATIONS
 Occlusion;
 Overgrown and undergrown segments;
 Shrunk or elongated shapes due to
movement.
 Variable sha...
SUMMARY
 Object-based approach: obvious choice as we are dealing with
objects not points;
 Connected component segmentat...
THANK YOU!
18
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Detection of temporary objects in mobile lidar point clouds

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Transcript of "Detection of temporary objects in mobile lidar point clouds"

  1. 1. Detection of Temporary Objects in Mobile Lidar Point Clouds Xinwei Fang Guorui Li Kourosh Khoshelham Sander Oude Elberink
  2. 2. BACKGROUND  Mobile laser scanning provides an accurate recording of road environments useful for many applications;  Usually only permanent objects are of interest;  Temporary objects can hamper the analysis of permanent ones;  Example: change detection using multi-epoch data where temporary objects appear as (false) change signals. 2
  3. 3. APPROACH Static  Temporary objects: Moving  Static temporary objects:  segmentation + classification based on shape features  Moving temporary objects:  segmentation + closest point analysis in two sensor data 3
  4. 4. STATIC TEMPORARY OBJECTS  Classification based on shape features: Linear & short Linear & tall Volumetric & tall Planar Volumetric & short 4
  5. 5. MOVING TEMPORARY OBJECTS  Detection based on closest point analysis with two sensor data Sensor 1 Sensor 2 5
  6. 6. METHODS 1. Ground removal 2. Connected-component segmentation 3. Static temporary objects:  feature extraction + classification 4. Moving temporary objects:  closest point analysis + classification 6
  7. 7. GROUND REMOVAL  Plane-based segmentation  Ground = large & low segment 7
  8. 8. CONNECTED COMPONENT SEGMENTATION  Points that are closer than a certain distance (e.g. 30 cm) belong to one connected component; 8
  9. 9. FEATURE EXTRACTION  Shape features Size (number of points) Area on horizontal plane Mean density Height of the lowest point Height  Contextual feature Distance to trajectory  Eigen-based features Anisotropy Planarity Sphericity Linearity 9
  10. 10. CLASSIFICATION  Ground truth for training and evaluation: manual labeling (115 samples)  Feature selection  Forward Selection (FS)  Backward Elimination (BE)  Classification  Linear Discrimnant Classifier (LDC)  Support Vector Machine (SVM) 10
  11. 11. CLOSEST POINT ANALYSIS Static Moving Static objects Moving objects Number 45 24 11
  12. 12. EXPERIMENTS  Study area Enschede, urban  Data TopScan MLS One strip: ~20 mio points 12
  13. 13. RESULTS Classifier Completeness Correctness LDC - all features 0.90 0.86 LDC - FS (8 features) 0.90 0.79 LDC - BE (8 features) 0.95 0.91 SVM - all features 1.00 0.91 SVM - FS (9 features) 1.00 1.00 SVM - BE (11 features) 1.00 0.95  Detection results for static temporary objects (test set): 13
  14. 14. RESULTS  Classification results for the whole strip : 14
  15. 15. RESULTS Sensor 1 Sensor 2 Sensor1 + Sensor2 Completeness 0.93 0.86 0.90 Correctness 0.90 0.96 0.93 Overall Accuracy 0.84 0.83 0.84  Detection results for moving objects:  Total 64 samples  4 false positives  6 false negatives 15
  16. 16. LIMITATIONS  Occlusion;  Overgrown and undergrown segments;  Shrunk or elongated shapes due to movement.  Variable shape and size of vehicles. 16
  17. 17. SUMMARY  Object-based approach: obvious choice as we are dealing with objects not points;  Connected component segmentation of objects works well (but not perfect!);  Shape features are suitable for classification of static temporary objects; Accuracy > 90%.  Distance between closest points is a useful measure for detecting moving objects; Accuracy > 80%  Occlusion: objects seen by one sensor but not the other = problem for moving objects. 17
  18. 18. THANK YOU! 18
  19. 19. 19
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