Software & Data Downloads — StreetScene

Street Scene Dataset for evaluating our video anomaly detection algorithm.

The Street Scene dataset consists of 46 training video sequences and 35 testing video sequences taken from a static USB camera looking down on a scene of a two-lane street with bike lanes and pedestrian sidewalks. See Figure 1 for a typical frame from the dataset. Videos were collected from the camera at various times during two consecutive summers. All of the videos were taken during the daytime. The dataset is challenging because of the variety of activity taking place such as cars driving, turning, stopping and parking; pedestrians walking, jogging and pushing strollers; and bikers riding in bike lanes. In addition, the videos contain changing shadows, and moving background such as a flag and trees blowing in the wind.

There are a total of 203,257 color video frames (56,847 for training and 146,410 for testing) each of size 1280 x 720 pixels. The frames were extracted from the original videos at 15 frames per second.

The 35 testing sequences have a total of 205 anomalous events consisting of 17 different anomaly types. A complete list of anomaly types and the number of each in the test set can be found in our paper.

Ground truth annotations are provided for each testing video in the form of bounding boxes around each anomalous event in each frame. Each bounding box is also labeled with a track number, meaning each anomalous event is labeled as a track of bounding boxes. Track lengths vary from tens of frames to 5200 which is the length of the longest testing sequence. A single frame can have more than one anomaly labeled.

    •  Ramachandra, B., Jones, M.J., "Street Scene: A new dataset and evaluation protocol for video anomaly detection", IEEE Winter Conference on Applications of Computer Vision (WACV), DOI: 10.1109/​WACV45572.2020.9093457, February 2020, pp. 2569-2578.
      BibTeX TR2020-017 PDF Data
      • @inproceedings{Jones2020feb2,
      • author = {Ramachandra, Bharathkumar and Jones, Michael J.},
      • title = {Street Scene: A new dataset and evaluation protocol for video anomaly detection},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2020,
      • pages = {2569--2578},
      • month = feb,
      • doi = {10.1109/WACV45572.2020.9093457},
      • url = {https://www.merl.com/publications/TR2020-017}
      • }
    •  Jones, M.J., Ramachandra, B., "Street Scene: A new dataset and evaluation protocol for video anomaly detection", arXiv, February 2019.
      BibTeX arXiv Data Data
      • @article{Jones2019feb,
      • author = {Jones, Michael J. and Ramachandra, Bharathkumar},
      • title = {Street Scene: A new dataset and evaluation protocol for video anomaly detection},
      • journal = {arXiv},
      • year = 2019,
      • month = feb,
      • url = {https://arxiv.org/abs/1902.05872v2}
      • }
    •  Jones, M.J., Ramachandra, B., "Street Scene: A new dataset and evaluation protocol for video anomaly detection", arXiv, January 2018.
      BibTeX arXiv Data Data
      • @article{Jones2018jan,
      • author = {Jones, Michael J. and Ramachandra, Bharathkumar},
      • title = {Street Scene: A new dataset and evaluation protocol for video anomaly detection},
      • journal = {arXiv},
      • year = 2018,
      • month = jan,
      • url = {https://arxiv.org/abs/1902.05872v1}
      • }

    Access data at https://doi.org/10.5281/zenodo.10870471.