1. Feature Extraction Conor Mc Elhinney Monday 14th February 2011

3. Road extraction

4. Roadside feature extraction

6. Road extraction

7. Roadside feature extraction

9. high accuracy ( > 90%)

10. easy verification / validation in software (manual)

12. Road extraction

13. Roadside feature extraction

15. automated road edge error removal / processing

17. automated road edge error removal / processing

20. Select nav points from db

21. Select those nav points separated by a specific interval1m

23. Select nav points from db

24. Select those nav points separated by a specific interval

25. Can create cross sections orthogonal to these nav points and the vans heading1m

27. Select nav points from db

28. Select those nav points separated by a specific interval

29. Can create cross sections orthogonal to these nav points and the vans heading

30. Take a cross section1m

32. Select nav points from db

33. Select those nav points separated by a specific interval

34. Can create cross sections orthogonal to these nav points and the vans heading

35. Take a cross section

37. Select nav points from db

38. Select those nav points separated by a specific interval

39. Can create cross sections orthogonal to these nav points and the vans heading

40. Take a cross section

41. Find edge based on slope

43. Select nav points from db

44. Select those nav points separated by a specific interval

45. Can create cross sections orthogonal to these nav points and the vans heading

46. Take a cross section

47. Find edge based on slope

48. Refine based on pulse width and amplitude

50. Select nav points from db

51. Select those nav points separated by a specific interval

52. Can create cross sections orthogonal to these nav points and the vans heading

53. Take a cross section

54. Find edge based on slope

55. Refine based on pulse width and amplitude

56. Return edgesIterate for all nav points

58. Select nav points from db

59. Select those nav points separated by a specific interval

60. Can create cross sections orthogonal to these nav points and the vans heading

61. Take a cross section

62. Find edge based on slope

63. Refine based on pulse width and amplitude

64. Return edgesIterate for all nav points

66. Select nav points from db

67. Select those nav points separated by a specific interval

68. Can create cross sections orthogonal to these nav points and the vans heading

69. Take a cross section

70. Find edge based on slope

71. Refine based on pulse width and amplitude

72. Return edgesIterate for all nav points

75. Regional road containing 2-3 lanes.

76. Left edge in results is excellent

77. Right edge is quite good.

78. lower density

79. more obstructions (cars / islands)

81. Road Edges – 300m straight

82. Road Edges – 300m straight

83. Road Edges – Bend

84. Road Edges – Bend

85. Road Edges – layby

86. Road Edges – layby

87. Road Edges – Roundabout

88. Road Edges – Roundabout

89. Road Edges – Multilane with layby

90. Road Edges – Multilane with layby

91. Road Edges – Right Edge not perfect

92. Road Edges – Right Edge not perfect

93. Road Surface

94. Road Surface

95. Road Surface

96. Road Surface

98. Need to integrate error processing of right edge into the algorithm.

99. Accuracy assessment of edges

100. Quantitative assessment of road edge algorithms doesn’t exist. We intend to develop a line comparison based approach as point based comparison involves too much error.

102. Road extraction

103. Roadside feature extraction

105. Automate pole extraction

106. Automate tree extraction

107. Determine viability of this approach to other roadside features.

109. Automate pole extraction

110. Automate tree extraction

111. Determine viability of this approach to other roadside features.

114. Take a cross section (50m x 20m)

116. Take a cross section (50m x 20m)

117. Cluster data using region growing

119. Take a cross section (50m x 20m)

120. Cluster data using region growing

121. Remove “ground” region

123. Take a cross section (50m x 20m)

124. Cluster data using region growing

125. Remove “ground” region

126. Process segments into grouped objectsIterate for all cross sections in survey

129. Pole extraction examples – lights

130. Pole extraction examples – signpost

131. Pole extraction examples – signpost

132. Pole extraction examples – Telegraph pole

133. Pole extraction examples – Telegraph pole

134. Pole extraction examples – Signs with two pole bases

135. Pole extraction examples – Signs with two pole bases

140. The initial classification of roadside objects works very well.

141. We need to develop new extractors/detectors for linear features like walls/fences and so on.

143. The initial classification of roadside objects works very well.

144. We need to develop new extractors/detectors for linear features like walls/fences and so on.

145. The initial results of the object finder are very promisingWall

146. Other features – Fences / walls/ barriers Crash barrier

147. Other features – Fences / walls/ barriers Fence

148. Other features – Fences / walls/ barriers

149. Other features – Fences / walls/ barriers Wall

150. Other features – Fences / walls/ barriers Crash barrier

151. Other features – Fences / walls/ barriers Fence

152. Other features – Bridges

153. Other features – Bridges

155. Once this is complete we can work on pole classification, i.e differentiate between signs, lightposts, telegraph poles....

157. Road extraction

158. Roadside feature extraction