| | --- |
| | license: mit |
| | tags: |
| | - object-detection |
| | - computer-vision |
| | - sort |
| | - tracker |
| | - ocsort |
| | --- |
| | |
| | ### Model Description |
| | [Sort](https://arxiv.org/abs/1602.00763): A simple online and realtime tracking algorithm for 2D multiple object tracking in video sequences<img src="https://raw.githubusercontent.com/noahcao/OC_SORT/master/assets/teaser.png" width="600"/> |
| |
|
| | ### Installation |
| | ``` |
| | pip install sort-track |
| | ``` |
| |
|
| | ### Tracker |
| | ```python |
| | from sort.tracker import SortTracker |
| | |
| | tracker = SortTracker(args) |
| | for image in images: |
| | dets = detector(image) |
| | online_targets = tracker.update(dets) |
| | ``` |
| |
|
| | ### BibTeX Entry and Citation Info |
| | ``` |
| | @inproceedings{Bewley2016_sort, |
| | author={Bewley, Alex and Ge, Zongyuan and Ott, Lionel and Ramos, Fabio and Upcroft, Ben}, |
| | booktitle={2016 IEEE International Conference on Image Processing (ICIP)}, |
| | title={Simple online and realtime tracking}, |
| | year={2016}, |
| | pages={3464-3468}, |
| | keywords={Benchmark testing;Complexity theory;Detectors;Kalman filters;Target tracking;Visualization;Computer Vision;Data Association;Detection;Multiple Object Tracking}, |
| | doi={10.1109/ICIP.2016.7533003} |
| | } |
| | ``` |
