| *Important.* This dataset is part of the [**torchmil** library](http://torchmil.readthedocs.io/). |
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| Traffic Anomaly Detection (TAD) dataset adapted for Multiple Instance Learning (MIL). |
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| ### About the Original Traffic Anomaly Detection (TAD) Dataset |
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| The original [Traffic Anomaly Detection (TAD) dataset](https://www.kaggle.com/datasets/nikanvasei/traffic-anomaly-dataset-tad) contains video clips. Each clip is labeled to indicate whether it contains an anomaly or not; however, frame-level labels are not available |
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| ### Dataset Description |
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| We have preprocessed the videos by computing features for each frame using various feature extractors. |
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| - A **video** is labeled as positive (`label=1`) if it contains evidence of traffic anomaly. |
| - A **video** is labeled as positive (`label=1`) if it contains at least one positive frame. |
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| This means a video is considered positive if there is any evidence of traffic anomaly. |
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| ### Directory structure |
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| The following directory structure is expected: |
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| ``` |
| root |
| ├── features |
| │ ├── features_{features} |
| │ │ ├── video1.npy |
| │ │ ├── video2.npy |
| │ │ └── ... |
| ├── labels |
| │ ├── video1.npy |
| │ ├── video2.npy |
| │ └── ... |
| └── splits.csv |
| ``` |
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| Each `.npy` file corresponds to a video. The `splits.csv` file defines train/test splits for standardized experimentation. |
