SOMPT22

Multi-Object Tracking Dataset.

Fatih Emre Simsek1,2 Cevahir Cigla1 Koray Kayabol2

1Aselsan Inc. 2Gebze Technical University

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Abstract

Multi-object tracking (MOT) has been dominated by the use of track by detection approaches due to the success of convolutional neural networks (CNNs) on detection in the last decade. As the datasets and bench-marking sites are published, research direction has shifted towards yielding best accuracy on generic scenarios including re-identification (reID) of objects while tracking. In this study, we narrow the scope of MOT for surveillance by providing a dedicated dataset of pedestrians and focus on in-depth analyses of well performing multi-object trackers to observe the weak and strong sides of state-of-the-art (SOTA) techniques for real-world applications. For this purpose, we introduce SOMPT22 dataset; a new set for multi person tracking with annotated short videos captured from static cameras located on poles with 6-8 meters in height positioned for city surveillance. This provides a more focused and specific benchmarking of MOT for outdoor surveillance compared to public MOT datasets. We analyze MOT trackers classified as one-shot and two-stage with respect to the way of use of detection and reID networks on this new dataset. The experimental results of our new dataset indicate that SOTA is still far from high efficiency, and single-shot trackers are good candidates to unify fast execution and accuracy with competitive performance.

Dataset

Download the dataset from Google Drive

Detection & Tracking Datasets

SOMPT22 Statistics

Experiment Setup

Benchmark Results

Citation

@misc{https://doi.org/10.48550/arxiv.2208.02580,
  doi = {10.48550/ARXIV.2208.02580},
  url = {https://arxiv.org/abs/2208.02580},
  author = {Simsek, Fatih Emre and Cigla, Cevahir and Kayabol, Koray},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}

License

The annotations of SOMPT22 are licensed under a Creative Commons Attribution 4.0 License. The dataset of SOMPT22 is available for non-commercial research purposes only. All videos and images of SOMPT22 are obtained from the Internet.