Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System
Table 1
Extraction results of mixed feature parameters of vehicle targets from some testing samples.
T1
T2
T3
T4
T5
0.9338
0.3577
0.6078
0.4553
0.6343
0.9606
0.3563
0.3757
0.4733
0.6685
0.9086
0.4176
0.6561
0.4506
0.6498
0.8101
0.5863
0.7398
0.4723
0.6733
0.9271
0.3748
0.6214
0.4476
0.6321
┆
┆
┆
┆
┆
T6
T7
T8
T9
T10
0.8264
0.8264
1
0.8841
0.3266
0.8138
0.8109
1
0.8815
0.4189
0.8048
0.8216
1
0.8838
0.3595
0.8197
0.8096
1
0.8792
0.4214
0.8395
0.8211
1
0.8744
0.3369
┆
┆
┆
┆
┆
T11
T12
T13
T14
T15
0.6238
0.1856
0.0002
0.0244
0.0731
0.5203
0.2703
0.0992
0.0677
0.0508
0.6167
0.2114
0.0002
0.0395
0.0802
0.5926
0.2637
0.0004
0.0759
0.0841
0.6221
0.1935
0.0002
0.0288
0.0753
┆
┆
┆
┆
┆
T16
T17
T18
T19
0.0467
3.8E-05
1.1623
0.648
0.0232
0.0438
5.243
0
0.0411
6.8E-05
4.4509
0.831
0.0182
0.0002
4.866
0.468
0.0454
2.1E-05
4.8596
0.513
┆
┆
┆
┆
Note. T1 represents the eccentricity of the region, T2 represents the ratio of the short axis to the long axis of the region, T3 represents the compactness of regions, T4 to T9 parameters represent independent invariant moment, T10 to T17 parameters represent discrete cosine descriptor, T18 represents the area of region, and T19 represents the perimeter of region.
