Street Scene Video Anomaly Detection Dataset

Introduction 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. 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 activities 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 202,545 color video frames (56,135 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. NOTE: This version of the dataset differs slightly with the original made available in 2020. Some anomalies were found in a few of the normal training sequences. These training frames were deleted from the dataset. Specifically, the following frames were removed: Train026: frames 1-184 (car taking a u-turn) Train027: frames 1-229 (jay walkers) Train031: frames 1-299 (jay walkers, illegally parked car) At a Glance The size of the unzipped dataset is ~46GB The dataset consists of Train sequences (containing only videos with normal activity), Test sequences (containing some anomalous activity) along with ground truth annotations, and a README.md file describing the data organization and ground truth annotation format. The zip file contains a Train directory, a Test directory and a README.md file. Other Resources None Citation If you use the Street Scene dataset in your research, please cite our contribution: @inproceedings{ramachandra2020street,
title={Street Scene: A new dataset and evaluation protocol for video anomaly detection},
author={Ramachandra, Bharathkumar and Jones, Michael},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={2569--2578},
year={2020}
}
License The Street Scene dataset is released under CC-BY-SA-4.0 license. All data: Created by Mitsubishi Electric Research Laboratories (MERL), 2023

SPDX-License-Identifier: CC-BY-SA-4.0