SOMPT22

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SOMPT22 is a multi-object tracking (MOT) benchmark focused on *surveillance-style pedestrian tracking*. * 22 long video sequences (static pole-mounted cameras, 6 – 8 m height) * ~51 k annotated frames with bounding boxes + unique track IDs * Outdoor scenes with illumination changes, partial occlusions and appearance similarity * Single class: *person* * Split files ready for training/validation and standard MOT evaluation tools SOMPT22 aims to complement generic MOTChallenge-style datasets by stressing long-term ID maintenance under sparse-to-medium crowd density instead of dense, short clips. *Homepage → https://sompt22.github.io* *Download → Google Drive link in the homepage* *Citation →* ```bibtex @misc{simsek2022sompt22, author = {Simsek, Fatih Emre and Cigla, Cevahir and Kayabol, Koray}, title = {SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset}, year = {2022}, eprint = {2208.02580}, archivePrefix = {arXiv}, primaryClass = {cs.CV} }