September 2019
tl;dr: detection results (bbox) tracking with kalman filter and Hungarian algorithm.
Classical methods for tracking reaches SOTA with faster RCNN. The performance gain is mainly from improved detection results.
The code is at this github repo.
- Only bbox info is used for tracking. No appearance features are included. (the appearance features are included in the deep-sort paper).
- Estimation model: Kalman filter with linear constant velocity model between frames, independent of other objects and camera motion. When a detection is associated with a target, the detected bbox is used to update the target state (velocity). If no detection, then the state is simply the predicted without correction.
- Data association: Hungarian algorithm with IOU thresh.
- Creation and deletion of tracked objects: any detection with overlap less than IOU thresh. Tracks are are terminated if they are not detected for 1 frame.
- Aspect ratio is considered constant.
- There are two schools of detection/tracking, detection by tracking and tacking by detection. SORT advocates tracking by detection to leverage the recent progress in DL in object detection.
- Two types of tracking
- in batch mode, where future data is also available during tracking.
- online mode, where only history data is available.
- Questions and notes on how to improve/revise the current work