SOMPT22 (Surveillance Oriented Multi-Pedestrian Tracking Dataset (SOMPT22))

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} }