filename
stringlengths
15
17
case
stringclasses
2 values
duration
float32
1.47
13.2
accident_000.mp4
accident
8.31
accident_001.mp4
accident
5.35
accident_0010.mp4
accident
6.71
accident_0011.mp4
accident
6.4
accident_0012.mp4
accident
7.08
accident_0013.mp4
accident
6.65
accident_0014.mp4
accident
5.88
accident_0015.mp4
accident
4.81
accident_0016.mp4
accident
4.33
accident_0017.mp4
accident
4.77
accident_0018.mp4
accident
7.57
accident_0019.mp4
accident
6.4
accident_002.mp4
accident
4.18
accident_0020.mp4
accident
7.63
accident_0021.mp4
accident
6.37
accident_0022.mp4
accident
9.67
accident_0023.mp4
accident
12.5
accident_0024.mp4
accident
4.37
accident_0025.mp4
accident
7
accident_0026.mp4
accident
7.95
accident_0027.mp4
accident
4.11
accident_0028.mp4
accident
7.88
accident_0029.mp4
accident
7.31
accident_003.mp4
accident
8.05
accident_0030.mp4
accident
7.45
accident_0031.mp4
accident
5.11
accident_0032.mp4
accident
13.15
accident_0033.mp4
accident
6.88
accident_0034.mp4
accident
5.01
accident_0035.mp4
accident
2.55
accident_0036.mp4
accident
4.11
accident_0037.mp4
accident
3.07
accident_0038.mp4
accident
2.13
accident_0039.mp4
accident
1.97
accident_004.mp4
accident
7.18
accident_0040.mp4
accident
3.6
accident_0041.mp4
accident
2.43
accident_0042.mp4
accident
2.43
accident_0043.mp4
accident
4.23
accident_0044.mp4
accident
3.63
accident_0045.mp4
accident
2.93
accident_0046.mp4
accident
1.47
accident_0047.mp4
accident
2.83
accident_0048.mp4
accident
3.85
accident_0049.mp4
accident
2.35
accident_005.mp4
accident
9.01
accident_0050.mp4
accident
3.81
accident_0051.mp4
accident
4.55
accident_0052.mp4
accident
2.71
accident_0053.mp4
accident
3.31
accident_0054.mp4
accident
3.78
accident_0055.mp4
accident
3.88
accident_0056.mp4
accident
5.48
accident_0057.mp4
accident
3.38
accident_0058.mp4
accident
3.44
accident_0059.mp4
accident
4.38
accident_006.mp4
accident
6.28
accident_0060.mp4
accident
4.48
accident_0061.mp4
accident
1.78
accident_0062.mp4
accident
4.35
accident_0063.mp4
accident
4.61
accident_0064.mp4
accident
3.05
accident_0065.mp4
accident
4.75
accident_0066.mp4
accident
4.38
accident_0067.mp4
accident
3.95
accident_007.mp4
accident
8.35
accident_008.mp4
accident
6.81
accident_009.mp4
accident
5.88
driving_000.mp4
driving
5.72
driving_001.mp4
driving
5.72
driving_0010.mp4
driving
5.72
driving_00100.mp4
driving
5.72
driving_00101.mp4
driving
5.72
driving_00102.mp4
driving
5.72
driving_00103.mp4
driving
5.72
driving_00104.mp4
driving
4.11
driving_00105.mp4
driving
6.21
driving_00106.mp4
driving
4.91
driving_00107.mp4
driving
6.21
driving_00108.mp4
driving
4.38
driving_00109.mp4
driving
4.75
driving_0011.mp4
driving
5.72
driving_00110.mp4
driving
7.31
driving_00111.mp4
driving
5.28
driving_00112.mp4
driving
5.45
driving_00113.mp4
driving
5.35
driving_00114.mp4
driving
4.51
driving_00115.mp4
driving
6.15
driving_00116.mp4
driving
5.18
driving_00117.mp4
driving
6.28
driving_00118.mp4
driving
5.75
driving_00119.mp4
driving
5.66
driving_0012.mp4
driving
5.72
driving_00120.mp4
driving
1.65
driving_00121.mp4
driving
3.72
driving_00122.mp4
driving
5.15
driving_00123.mp4
driving
6.71
driving_00124.mp4
driving
6.48
driving_00125.mp4
driving
6.35
driving_0013.mp4
driving
5.72

Dataset Summary

The dataset consisted of 2 types of cases; accident and driving while riding a motorcycle. 68 accident cases and 68 driving cases are prepared. 30 fps and 852x480 by default. It might be helpful when you train a model to infer whether a video is a motorcycle crash or not. One thing you should know about is 'driving videos' are not typically motorcycle driving. Most 'driving videos' are dashcams in the car. However, all the videos about accidents are motorcycle traffic accidents.

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