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  1. UBnormal/README.md +142 -0
  2. UBnormal/hr_bool_masks/README.md +34 -0
  3. UBnormal/hr_bool_masks/testing/hr_mask.npy +3 -0
  4. UBnormal/hr_bool_masks/testing/stats.json +1700 -0
  5. UBnormal/hr_bool_masks/testing/test_frame_mask/001_0100.npy +3 -0
  6. UBnormal/hr_bool_masks/testing/test_frame_mask/001_0200.npy +3 -0
  7. UBnormal/hr_bool_masks/testing/test_frame_mask/001_0400.npy +3 -0
  8. UBnormal/hr_bool_masks/testing/test_frame_mask/001_0500.npy +3 -0
  9. UBnormal/hr_bool_masks/testing/test_frame_mask/001_0600.npy +3 -0
  10. UBnormal/hr_bool_masks/testing/test_frame_mask/001_0700.npy +3 -0
  11. UBnormal/hr_bool_masks/testing/test_frame_mask/001_0800.npy +3 -0
  12. UBnormal/hr_bool_masks/testing/test_frame_mask/001_0900.npy +3 -0
  13. UBnormal/hr_bool_masks/testing/test_frame_mask/002_0100.npy +3 -0
  14. UBnormal/hr_bool_masks/testing/test_frame_mask/002_0400.npy +3 -0
  15. UBnormal/hr_bool_masks/testing/test_frame_mask/002_0500.npy +3 -0
  16. UBnormal/hr_bool_masks/testing/test_frame_mask/002_0600.npy +3 -0
  17. UBnormal/hr_bool_masks/testing/test_frame_mask/003_0100.npy +3 -0
  18. UBnormal/hr_bool_masks/testing/test_frame_mask/003_0200.npy +3 -0
  19. UBnormal/hr_bool_masks/testing/test_frame_mask/003_0600.npy +3 -0
  20. UBnormal/hr_bool_masks/testing/test_frame_mask/004_0100.npy +3 -0
  21. UBnormal/hr_bool_masks/testing/test_frame_mask/004_0152.npy +3 -0
  22. UBnormal/hr_bool_masks/testing/test_frame_mask/004_0252.npy +3 -0
  23. UBnormal/hr_bool_masks/testing/test_frame_mask/004_0400.npy +3 -0
  24. UBnormal/hr_bool_masks/testing/test_frame_mask/004_0600.npy +3 -0
  25. UBnormal/hr_bool_masks/testing/test_frame_mask/005_0153.npy +3 -0
  26. UBnormal/hr_bool_masks/testing/test_frame_mask/005_0253.npy +3 -0
  27. UBnormal/hr_bool_masks/testing/test_frame_mask/005_0400.npy +3 -0
  28. UBnormal/hr_bool_masks/testing/test_frame_mask/005_0500.npy +3 -0
  29. UBnormal/hr_bool_masks/testing/test_frame_mask/005_0700.npy +3 -0
  30. UBnormal/hr_bool_masks/testing/test_frame_mask/005_0800.npy +3 -0
  31. UBnormal/hr_bool_masks/testing/test_frame_mask/006_0200.npy +3 -0
  32. UBnormal/hr_bool_masks/testing/test_frame_mask/006_0300.npy +3 -0
  33. UBnormal/hr_bool_masks/testing/test_frame_mask/006_0800.npy +3 -0
  34. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0100.npy +3 -0
  35. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0151.npy +3 -0
  36. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0152.npy +3 -0
  37. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0200.npy +3 -0
  38. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0251.npy +3 -0
  39. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0253.npy +3 -0
  40. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0300.npy +3 -0
  41. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0351.npy +3 -0
  42. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0400.npy +3 -0
  43. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0451.npy +3 -0
  44. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0500.npy +3 -0
  45. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0600.npy +3 -0
  46. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0700.npy +3 -0
  47. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0800.npy +3 -0
  48. UBnormal/hr_bool_masks/testing/test_frame_mask/007_0900.npy +3 -0
  49. UBnormal/hr_bool_masks/testing/test_frame_mask/007_1000.npy +3 -0
  50. UBnormal/hr_bool_masks/testing/test_frame_mask/008_0100.npy +3 -0
UBnormal/README.md ADDED
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1
+ # UBnormal dataset | [COSKAD](https://github.com/aleflabo/COSKAD) | [Original Repository](https://github.com/lilygeorgescu/UBnormal)
2
+ ---
3
+
4
+ Skeletal version proposed in
5
+
6
+ **[Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection](https://arxiv.org/abs/2301.09489)**</br>
7
+ *Alessandro Flaborea\*, Guido D'Amely\*, Stefano D'Arrigo\*, Marco Aurelio Sterpa, Alessio Sampieri, Fabio Galasso*
8
+ ---
9
+
10
+ We propose HR-UBnormal as an extension of the original UBnormal dataset [\[1\]]((#references)) with kinematic motion representations and a selected set of anomalies that relate only to human behaviors.
11
+ First, AlphaPose [\[2\]](#references) was used to extract the poses, and PoseFlow [\[3\]](#references) was used to track the skeletons throughout each video.
12
+ Then, we filtered out the non-human related anomalies. We removed the sub-sequences in which the only anomalous object was not a person (e.g., a car) or the anomaly cannot be detected using only body poses (e.g., fire in the scene).
13
+ As a result, we left the validation set unaltered while eliminating the frames 2.32% of the test set.
14
+
15
+
16
+ ## Notes regarding the file names' format
17
+
18
+ The UBnormal dataset annotated with the skeletal representation and its Human-Related version (HR) are released with the following directory structure:
19
+
20
+ ```
21
+ UBnormal
22
+ |
23
+ |__________ hr_bool_masks
24
+ | |
25
+ | |__________ testing
26
+ | | |
27
+ | | |__________ test_frame_mask
28
+ | | |_______________{scene_id}_{clip_id}.npy
29
+ | | |_______________...
30
+ | | |_______________{scene_id}_{clip_id}.npy
31
+ | |
32
+ | |__________ validating
33
+ | |
34
+ | |__________ test_frame_mask
35
+ | |_______________{scene_id}_{clip_id}.npy
36
+ | |_______________...
37
+ | |_______________{scene_id}_{clip_id}.npy
38
+ |
39
+ |__________ training
40
+ | |
41
+ | |__________ trajectories
42
+ | |
43
+ | |_________{scene_id}_{clip_id}
44
+ | |
45
+ | |_________00001.csv
46
+ | |_________...
47
+ | |_________0000{n}.csv
48
+ |
49
+ |__________ testing
50
+ | |
51
+ | |__________ trajectories
52
+ | | |
53
+ | | |_________{scene_id}_{clip_id}
54
+ | | |
55
+ | | |_________00001.csv
56
+ | | |_________...
57
+ | | |_________0000{n}.csv
58
+ | |
59
+ | |__________ test_frame_mask
60
+ | |
61
+ | |_______________{scene_id}_{clip_id}.npy
62
+ | |_______________...
63
+ | |_______________{scene_id}_{clip_id}.npy
64
+ |
65
+ |__________ validating
66
+ |
67
+ |__________ trajectories
68
+ | |
69
+ | |_________{scene_id}_{clip_id}
70
+ | |
71
+ | |_________00001.csv
72
+ | |_________...
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+ | |_________0000{n}.csv
74
+ |
75
+ |__________ test_frame_mask
76
+ |
77
+ |_______________{scene_id}_{clip_id}.npy
78
+ |_______________...
79
+ |_______________{scene_id}_{clip_id}.npy
80
+ ```
81
+
82
+ In the `hr_bool_masks`, the frames which were anomalous in the original version but where the anomaly didn't involve any human being are toggled to 'normal', i.e., they are toggled from 1 to 0.
83
+
84
+ Regarding the naming of the files, since our code expects the `scene_id` and the `clip_id` to be integers and some of the file names in the original dataset were overloaded, the following mapping has been adopted:
85
+
86
+ -
87
+ ```
88
+ scene_id = {c1c2c3}
89
+ ```
90
+
91
+ where `c1` is the scene type (`{'abnormal':0, 'normal':1}`) and `c2c3` is the scene number of the corresponding file in the original dataset.
92
+
93
+ -
94
+ ```
95
+ clip_id = {c1c2c3c4}
96
+ ```
97
+
98
+ where `c1c2` is the scenario number (i.e., clip id) of the corresponding file in the original dataset, `c3c4` is the remaining id part dubbed as version. Indeed, in the original dataset some videos have names as in the following example:
99
+
100
+ - `normal_scene_1_scenario1_1`
101
+ - `normal_scene_1_scenario1_10`
102
+ - `abnormal_scene_9_scenario_1_fog`
103
+ - `abnormal_scene_12_scenario_1_smoke`
104
+
105
+ In order to keep the information regarding the environment in the clip (e.g., fog, smoke, ...), this mapping has been adopted:
106
+
107
+ ```
108
+ {'fog': 51, 'fire': 52, 'smoke': 53}
109
+ ```
110
+
111
+
112
+ ## Citation
113
+
114
+ If you find this dataset useful in your research, please consider cite:
115
+
116
+ ```
117
+ @misc{flaborea2023contracting,
118
+ title={Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection},
119
+ author={Alessandro Flaborea and Guido D'Amely and Stefano D'Arrigo and Marco Aurelio Sterpa and Alessio Sampieri and Fabio Galasso},
120
+ year={2023},
121
+ eprint={2301.09489},
122
+ archivePrefix={arXiv},
123
+ primaryClass={cs.CV}
124
+ }
125
+
126
+ @InProceedings{Acsintoae_CVPR_2022,
127
+ author = {Andra Acsintoae and Andrei Florescu and Mariana{-}Iuliana Georgescu and Tudor Mare and Paul Sumedrea and Radu Tudor Ionescu and Fahad Shahbaz Khan and Mubarak Shah},
128
+ title = {UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection},
129
+ booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
130
+ month = {June},
131
+ year = {2022},
132
+ }
133
+ ```
134
+
135
+
136
+ ## References
137
+
138
+ [1] A. Acsintoae, A. Florescu, M.-I. Georgescu, T. Mare, P. Sumedrea, R. T. Ionescu, F. S. Khan, M. Shah, Ubnormal: New benchmark for supervised open-set video anomaly detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp.20143–20153.
139
+
140
+ [2] H.-S. Fang, S. Xie, Y.-W. Tai, C. Lu, Rmpe: Regional multi-person pose estimation, in: ICCV, 2017, pp. 2334–2343.
141
+
142
+ [3] Y. Xiu, J. Li, H. Wang, Y. Fang, C. Lu, Pose Flow: Efficient online pose tracking, in: BMVC, 2018.
UBnormal/hr_bool_masks/README.md ADDED
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1
+ # Boolean masks of the UBnormal Human-Related subset
2
+
3
+ The folders `testing` and `validating` contain the boolean masks for the test set and the validation set, respectively.
4
+
5
+ Within each folder, a `hr_mask.npy` is provided, which contains the masks of the clips concatenated by lexicographic order, i.e. in the same order as their names are listed in the folder.
6
+
7
+ The folders `{testing, validating}/test_frame_mask` contain the masks for each clip as separated Numpy files. Values within the masks are boolean such that
8
+
9
+ ```
10
+ mask[i] = True if the frame should be kept
11
+ mask[i] = False otherwise
12
+ ```
13
+
14
+ For each set, a `stats.json` file shows the total number of frames in the set, the number of discarded frames and the percentage of discarded frames; the same information is also reported for each clip.
15
+
16
+ To use the HR mask, add in the evaluation code the following:
17
+
18
+ ```python
19
+ import numpy as np
20
+
21
+ HR_UBNORMAL_MASK = np.load('data/UBnormal/hr_bool_masks/testing/hr_mask.npy')
22
+ ```
23
+
24
+ if the clips in the evaluation are **loaded and appear in the code in the same order as their names are sorted in the folder**, otherwise
25
+
26
+
27
+ ```python
28
+ import numpy as np
29
+
30
+ # example for the clip 001_0100
31
+ HR_UBNORMAL_MASK_001_0100 = np.load('data/UBnormal/hr_bool_masks/testing/test_frame_mask/001_0100.npy')
32
+ ```
33
+
34
+ for each clip.
UBnormal/hr_bool_masks/testing/hr_mask.npy ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:bddcd2a3fdc170214c65f6e57b2216b4d89b7a0227139d00cecd5f154cd07f68
3
+ size 92768
UBnormal/hr_bool_masks/testing/stats.json ADDED
@@ -0,0 +1,1700 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "global_stats": {
3
+ "total_number_video_frames": 92640,
4
+ "discarded_frames": 2151,
5
+ "percentage_discarded": 2.32,
6
+ "total_normal_frames": 42982,
7
+ "total_anomalous_frames": 47507,
8
+ "percentage_anomalous": 52.5
9
+ },
10
+ "details": {
11
+ "001_0100.npy": {
12
+ "number_video_frames": 131,
13
+ "discarded_frames": 0,
14
+ "percentage_discarded": 0.0,
15
+ "normal_frames": 63,
16
+ "anomalous_frames": 68,
17
+ "percentage_anomalous": 51.91
18
+ },
19
+ "001_0200.npy": {
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+ "number_video_frames": 151,
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+ "discarded_frames": 0,
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+ "percentage_discarded": 0.0,
23
+ "normal_frames": 76,
24
+ "anomalous_frames": 75,
25
+ "percentage_anomalous": 49.67
26
+ },
27
+ "001_0400.npy": {
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+ "number_video_frames": 451,
29
+ "discarded_frames": 0,
30
+ "percentage_discarded": 0.0,
31
+ "normal_frames": 316,
32
+ "anomalous_frames": 135,
33
+ "percentage_anomalous": 29.93
34
+ },
35
+ "001_0500.npy": {
36
+ "number_video_frames": 451,
37
+ "discarded_frames": 0,
38
+ "percentage_discarded": 0.0,
39
+ "normal_frames": 384,
40
+ "anomalous_frames": 67,
41
+ "percentage_anomalous": 14.86
42
+ },
43
+ "001_0600.npy": {
44
+ "number_video_frames": 151,
45
+ "discarded_frames": 0,
46
+ "percentage_discarded": 0.0,
47
+ "normal_frames": 92,
48
+ "anomalous_frames": 59,
49
+ "percentage_anomalous": 39.07
50
+ },
51
+ "001_0700.npy": {
52
+ "number_video_frames": 151,
53
+ "discarded_frames": 0,
54
+ "percentage_discarded": 0.0,
55
+ "normal_frames": 104,
56
+ "anomalous_frames": 47,
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+ "percentage_anomalous": 31.13
58
+ },
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+ "001_0800.npy": {
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61
+ "discarded_frames": 0,
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+ "percentage_discarded": 0.0,
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+ "normal_frames": 94,
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66
+ },
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+ "001_0900.npy": {
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+ "percentage_discarded": 0.0,
71
+ "normal_frames": 398,
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+ "anomalous_frames": 53,
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74
+ },
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+ "002_0100.npy": {
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+ "percentage_discarded": 0.0,
79
+ "normal_frames": 48,
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82
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