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