Datasets:

Rahima411
/

ucf-anomaly-detection-mapped

video_id stringlengths 19 35	video_key stringlengths 13 22	category stringclasses 13 values	features_shape listlengths 2 2	labels_shape listlengths 1 1	duration float64 3.47 4.73k	split stringclasses 3 values	num_anomaly_segments int64 1 177	has_sentences bool 1 class
normal/Normal_Videos_439_x264	Normal_Videos_439_x264	normal	[ 7, 1024 ]	[ 7 ]	140.9	Train	3	true
normal/Normal_Videos_345_x264	Normal_Videos_345_x264	normal	[ 7, 1024 ]	[ 7 ]	7.04	Train	2	true
normal/Normal_Videos_704_x264	Normal_Videos_704_x264	normal	[ 7, 1024 ]	[ 7 ]	56.48	Train	3	true
normal/Normal_Videos_452_x264	Normal_Videos_452_x264	normal	[ 7, 1024 ]	[ 7 ]	14.84	Train	1	true
normal/Normal_Videos_576_x264	Normal_Videos_576_x264	normal	[ 7, 1024 ]	[ 7 ]	375.89	Train	6	true
normal/Normal_Videos_877_x264	Normal_Videos_877_x264	normal	[ 7, 1024 ]	[ 7 ]	334.18	Train	23	true
normal/Normal_Videos_015_x264	Normal_Videos_015_x264	normal	[ 7, 1024 ]	[ 7 ]	16.05	Train	4	true
normal/Normal_Videos_603_x264	Normal_Videos_603_x264	normal	[ 7, 1024 ]	[ 7 ]	109.27	Train	6	true
normal/Normal_Videos_621_x264	Normal_Videos_621_x264	normal	[ 7, 1024 ]	[ 7 ]	160.08	Train	7	true
normal/Normal_Videos_453_x264	Normal_Videos_453_x264	normal	[ 7, 1024 ]	[ 7 ]	177.42	Train	5	true
normal/Normal_Videos_758_x264	Normal_Videos_758_x264	normal	[ 7, 1024 ]	[ 7 ]	53	Train	2	true
normal/Normal_Videos_246_x264	Normal_Videos_246_x264	normal	[ 7, 1024 ]	[ 7 ]	166.46	Train	1	true
normal/Normal_Videos_634_x264	Normal_Videos_634_x264	normal	[ 7, 1024 ]	[ 7 ]	448.68	Train	7	true
normal/Normal_Videos_913_x264	Normal_Videos_913_x264	normal	[ 7, 1024 ]	[ 7 ]	20.3	Train	2	true
normal/Normal_Videos_656_x264	Normal_Videos_656_x264	normal	[ 7, 1024 ]	[ 7 ]	60.56	Train	2	true
normal/Normal_Videos_360_x264	Normal_Videos_360_x264	normal	[ 7, 1024 ]	[ 7 ]	32.83	Train	7	true
normal/Normal_Videos_798_x264	Normal_Videos_798_x264	normal	[ 7, 1024 ]	[ 7 ]	200.03	Train	9	true
normal/Normal_Videos_905_x264	Normal_Videos_905_x264	normal	[ 7, 1024 ]	[ 7 ]	39.87	Train	4	true
normal/Normal_Videos_100_x264	Normal_Videos_100_x264	normal	[ 7, 1024 ]	[ 7 ]	20.95	Train	1	true
normal/Normal_Videos_914_x264	Normal_Videos_914_x264	normal	[ 7, 1024 ]	[ 7 ]	29.33	Train	2	true
normal/Normal_Videos_310_x264	Normal_Videos_310_x264	normal	[ 7, 1024 ]	[ 7 ]	83.99	Train	3	true
normal/Normal_Videos_317_x264	Normal_Videos_317_x264	normal	[ 7, 1024 ]	[ 7 ]	30.97	Train	3	true
normal/Normal_Videos_885_x264	Normal_Videos_885_x264	normal	[ 7, 1024 ]	[ 7 ]	15.88	Train	2	true
normal/Normal_Videos_828_x264	Normal_Videos_828_x264	normal	[ 7, 1024 ]	[ 7 ]	31.05	Train	2	true
normal/Normal_Videos_892_x264	Normal_Videos_892_x264	normal	[ 7, 1024 ]	[ 7 ]	59.03	Train	4	true
normal/Normal_Videos_696_x264	Normal_Videos_696_x264	normal	[ 7, 1024 ]	[ 7 ]	120.86	Train	5	true
normal/Normal_Videos_781_x264	Normal_Videos_781_x264	normal	[ 7, 1024 ]	[ 7 ]	132.56	Train	8	true
normal/Normal_Videos_929_x264	Normal_Videos_929_x264	normal	[ 7, 1024 ]	[ 7 ]	30.92	Test	3	true
normal/Normal_Videos_831_x264	Normal_Videos_831_x264	normal	[ 7, 1024 ]	[ 7 ]	14.98	Train	1	true
normal/Normal_Videos_641_x264	Normal_Videos_641_x264	normal	[ 7, 1024 ]	[ 7 ]	120.23	Train	9	true
normal/Normal_Videos_050_x264	Normal_Videos_050_x264	normal	[ 7, 1024 ]	[ 7 ]	139.95	Train	10	true
normal/Normal_Videos_129_x264	Normal_Videos_129_x264	normal	[ 7, 1024 ]	[ 7 ]	15.57	Train	1	true
normal/Normal_Videos_247_x264	Normal_Videos_247_x264	normal	[ 7, 1024 ]	[ 7 ]	273.74	Train	1	true
normal/Normal_Videos_745_x264	Normal_Videos_745_x264	normal	[ 7, 1024 ]	[ 7 ]	10.17	Train	2	true
normal/Normal_Videos_606_x264	Normal_Videos_606_x264	normal	[ 7, 1024 ]	[ 7 ]	41.15	Train	5	true
normal/Normal_Videos_722_x264	Normal_Videos_722_x264	normal	[ 7, 1024 ]	[ 7 ]	291.04	Train	5	true
normal/Normal_Videos_150_x264	Normal_Videos_150_x264	normal	[ 7, 1024 ]	[ 7 ]	28.84	Train	3	true
normal/Normal_Videos_597_x264	Normal_Videos_597_x264	normal	[ 7, 1024 ]	[ 7 ]	74.35	Train	8	true
normal/Normal_Videos_365_x264	Normal_Videos_365_x264	normal	[ 7, 1024 ]	[ 7 ]	220.96	Train	7	true
normal/Normal_Videos_352_x264	Normal_Videos_352_x264	normal	[ 7, 1024 ]	[ 7 ]	180.14	Train	6	true
normal/Normal_Videos_401_x264	Normal_Videos_401_x264	normal	[ 7, 1024 ]	[ 7 ]	54.24	Train	3	true
normal/Normal_Videos_912_x264	Normal_Videos_912_x264	normal	[ 7, 1024 ]	[ 7 ]	24.87	Train	1	true
normal/Normal_Videos_478_x264	Normal_Videos_478_x264	normal	[ 7, 1024 ]	[ 7 ]	150.07	Train	3	true
normal/Normal_Videos_289_x264	Normal_Videos_289_x264	normal	[ 7, 1024 ]	[ 7 ]	28.8	Train	2	true
normal/Normal_Videos_801_x264	Normal_Videos_801_x264	normal	[ 7, 1024 ]	[ 7 ]	91.49	Train	9	true
normal/Normal_Videos_248_x264	Normal_Videos_248_x264	normal	[ 7, 1024 ]	[ 7 ]	38.04	Train	2	true
normal/Normal_Videos_312_x264	Normal_Videos_312_x264	normal	[ 7, 1024 ]	[ 7 ]	42.03	Train	7	true
normal/Normal_Videos_881_x264	Normal_Videos_881_x264	normal	[ 7, 1024 ]	[ 7 ]	7.63	Train	1	true
normal/Normal_Videos_251_x264	Normal_Videos_251_x264	normal	[ 7, 1024 ]	[ 7 ]	13.53	Train	2	true
stealing/Stealing071_x264	Stealing071_x264	stealing	[ 7, 1024 ]	[ 7 ]	32.07	Train	3	true
stealing/Stealing091_x264	Stealing091_x264	stealing	[ 7, 1024 ]	[ 7 ]	20.16	Test	4	true
stealing/Stealing031_x264	Stealing031_x264	stealing	[ 7, 1024 ]	[ 7 ]	36.27	Train	4	true
stealing/Stealing101_x264	Stealing101_x264	stealing	[ 7, 1024 ]	[ 7 ]	84.82	Val	6	true
stealing/Stealing035_x264	Stealing035_x264	stealing	[ 7, 1024 ]	[ 7 ]	371.85	Train	6	true
stealing/Stealing042_x264	Stealing042_x264	stealing	[ 7, 1024 ]	[ 7 ]	140.09	Train	9	true
stealing/Stealing079_x264	Stealing079_x264	stealing	[ 7, 1024 ]	[ 7 ]	195.06	Test	28	true
stealing/Stealing100_x264	Stealing100_x264	stealing	[ 7, 1024 ]	[ 7 ]	511.91	Val	39	true
stealing/Stealing050_x264	Stealing050_x264	stealing	[ 7, 1024 ]	[ 7 ]	111.63	Train	3	true
stealing/Stealing072_x264	Stealing072_x264	stealing	[ 7, 1024 ]	[ 7 ]	387.21	Train	11	true
stealing/Stealing070_x264	Stealing070_x264	stealing	[ 7, 1024 ]	[ 7 ]	44.93	Train	4	true
stealing/Stealing081_x264	Stealing081_x264	stealing	[ 7, 1024 ]	[ 7 ]	42.01	Test	6	true
stealing/Stealing114_x264	Stealing114_x264	stealing	[ 7, 1024 ]	[ 7 ]	40.57	Val	4	true
stealing/Stealing010_x264	Stealing010_x264	stealing	[ 7, 1024 ]	[ 7 ]	101.1	Train	9	true
stealing/Stealing109_x264	Stealing109_x264	stealing	[ 7, 1024 ]	[ 7 ]	315.14	Val	14	true
stealing/Stealing029_x264	Stealing029_x264	stealing	[ 7, 1024 ]	[ 7 ]	13.58	Train	3	true
stealing/Stealing020_x264	Stealing020_x264	stealing	[ 7, 1024 ]	[ 7 ]	220.13	Train	16	true
stealing/Stealing054_x264	Stealing054_x264	stealing	[ 7, 1024 ]	[ 7 ]	88.98	Train	5	true
stealing/Stealing111_x264	Stealing111_x264	stealing	[ 7, 1024 ]	[ 7 ]	157.07	Val	6	true
stealing/Stealing110_x264	Stealing110_x264	stealing	[ 7, 1024 ]	[ 7 ]	58.4	Val	4	true
stealing/Stealing073_x264	Stealing073_x264	stealing	[ 7, 1024 ]	[ 7 ]	50.84	Train	3	true
stealing/Stealing058_x264	Stealing058_x264	stealing	[ 7, 1024 ]	[ 7 ]	166.39	Train	5	true
stealing/Stealing012_x264	Stealing012_x264	stealing	[ 7, 1024 ]	[ 7 ]	90.43	Train	7	true
stealing/Stealing015_x264	Stealing015_x264	stealing	[ 7, 1024 ]	[ 7 ]	60	Train	5	true
stealing/Stealing024_x264	Stealing024_x264	stealing	[ 7, 1024 ]	[ 7 ]	82.2	Train	4	true
stealing/Stealing068_x264	Stealing068_x264	stealing	[ 7, 1024 ]	[ 7 ]	228.67	Train	6	true
stealing/Stealing078_x264	Stealing078_x264	stealing	[ 7, 1024 ]	[ 7 ]	86.23	Test	7	true
stealing/Stealing009_x264	Stealing009_x264	stealing	[ 7, 1024 ]	[ 7 ]	53.8	Train	5	true
stealing/Stealing067_x264	Stealing067_x264	stealing	[ 7, 1024 ]	[ 7 ]	70	Train	4	true
stealing/Stealing046_x264	Stealing046_x264	stealing	[ 7, 1024 ]	[ 7 ]	343.6	Train	4	true
stealing/Stealing069_x264	Stealing069_x264	stealing	[ 7, 1024 ]	[ 7 ]	26.73	Train	3	true
stealing/Stealing002_x264	Stealing002_x264	stealing	[ 7, 1024 ]	[ 7 ]	117.23	Train	11	true
stealing/Stealing025_x264	Stealing025_x264	stealing	[ 7, 1024 ]	[ 7 ]	727.24	Train	48	true
stealing/Stealing011_x264	Stealing011_x264	stealing	[ 7, 1024 ]	[ 7 ]	123.9	Train	12	true
stealing/Stealing018_x264	Stealing018_x264	stealing	[ 7, 1024 ]	[ 7 ]	68.4	Train	7	true
stealing/Stealing066_x264	Stealing066_x264	stealing	[ 7, 1024 ]	[ 7 ]	42.72	Train	3	true
stealing/Stealing095_x264	Stealing095_x264	stealing	[ 7, 1024 ]	[ 7 ]	25.07	Val	2	true
stealing/Stealing051_x264	Stealing051_x264	stealing	[ 7, 1024 ]	[ 7 ]	111.04	Train	6	true
stealing/Stealing047_x264	Stealing047_x264	stealing	[ 7, 1024 ]	[ 7 ]	45.12	Train	3	true
stealing/Stealing104_x264	Stealing104_x264	stealing	[ 7, 1024 ]	[ 7 ]	105.09	Val	11	true
stealing/Stealing013_x264	Stealing013_x264	stealing	[ 7, 1024 ]	[ 7 ]	308.37	Train	13	true
stealing/Stealing112_x264	Stealing112_x264	stealing	[ 7, 1024 ]	[ 7 ]	70.05	Val	6	true
stealing/Stealing049_x264	Stealing049_x264	stealing	[ 7, 1024 ]	[ 7 ]	49.23	Train	2	true
stealing/Stealing087_x264	Stealing087_x264	stealing	[ 7, 1024 ]	[ 7 ]	164.7	Test	26	true
stealing/Stealing084_x264	Stealing084_x264	stealing	[ 7, 1024 ]	[ 7 ]	524.46	Test	31	true
stealing/Stealing059_x264	Stealing059_x264	stealing	[ 7, 1024 ]	[ 7 ]	74.52	Train	4	true
stealing/Stealing106_x264	Stealing106_x264	stealing	[ 7, 1024 ]	[ 7 ]	47.76	Val	5	true
stealing/Stealing053_x264	Stealing053_x264	stealing	[ 7, 1024 ]	[ 7 ]	435.26	Train	10	true
stealing/Stealing108_x264	Stealing108_x264	stealing	[ 7, 1024 ]	[ 7 ]	132.23	Val	9	true
stealing/Stealing023_x264	Stealing023_x264	stealing	[ 7, 1024 ]	[ 7 ]	169.17	Train	12	true
stealing/Stealing093_x264	Stealing093_x264	stealing	[ 7, 1024 ]	[ 7 ]	41.04	Val	4	true

End of preview. Expand in Data Studio

UCF-Crime: Precomputed I3D Features with Temporal Annotations

This dataset provides pre-extracted 1024-dimensional I3D RGB features along with frame-level temporal anomaly labels for videos from the UCF-Crime dataset.

Dataset Characteristics

Features

1024-dimensional I3D RGB feature vectors
Extracted from 64 uniformly sampled frames per video
Feature tensor shape: [64, 1024]

Temporal Annotations

Mapped from original anomaly intervals
Re-scaled to match the 64 sampled frames
Only videos with valid annotations are included

Coverage

Videos that contain complete temporal anomaly intervals
Suitable for supervised learning tasks

Recommended Usage

This dataset is ideal for:

Frame-level binary classification
Reconstruction-based anomaly detection
Temporal convolutional networks (TCN)
Transformer-based sequence models
Sequential anomaly scoring models

Since features are already extracted, experiments are lightweight and GPU-efficient.

Loading the Dataset

The Data Loader code has also been provided. Please refer to that.

Citation

@inproceedings{sultani2018real, title={Real-world Anomaly Detection in Surveillance Videos}, author={Sultani, Waqas and Chen, Chen and Shah, Mubarak}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={4469--4478}, year={2018} }

Downloads last month: 7

Size of downloaded dataset files:

278 kB

Size of the auto-converted Parquet files:

23.5 kB

Number of rows:

893