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Kalman-IOU Tracker

Python implementation of the Kalman-IOU Tracker.

This tracker is modified based on the original IOU Tracker, which is a simple, high-speed tracker that works really well on the UA-DETRAC dataset. We make use of a Kalman filter to estimate object location and speed together, and obtained incremental performance improvements over the original version.

The Kalman filter's capability of making predictions allows us to skip frames while still keeping track of the object. Skipping frames in a tracking-by-detection task means the detector will process significantly less frames. The Kalman-IOU Tracker, when used with the EB detector and configured to skip 2/3 of the frames, can run in real-time while outperforming the original IOU Tracker on the DETRAC-Train dataset.

This repo is predominantly based on the original IOU Tracker. Please consider citing their work:

@INPROCEEDINGS{1517Bochinski2017,
	AUTHOR = {Erik Bochinski and Volker Eiselein and Thomas Sikora},
	TITLE = {High-Speed Tracking-by-Detection Without Using Image Information},
	BOOKTITLE = {International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017},
	YEAR = {2017},
	MONTH = aug,
	ADDRESS = {Lecce, Italy},
	URL = {http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf},
	}

Results from DETRAC Dataset

To reproduce the reported results, download and extract the DETRAC-toolkit and the detections you want to evaluate. Download links for the EB detections are provided below. Clone this repository into "DETRAC-MOT-toolkit/trackers/". Follow the instructions to configure the toolkit for tracking evaluation and set the tracker name in "DETRAC_experiment.m":

tracker.trackerName = 'kiout';

and run the script.

Note that you still need a working python environment with numpy and pykalman installed. You should obtain something like the following results for the 'DETRAC-Train' set:

DETRAC-Train Results with EB Detector

Tracker Frames PR-MT PR-PT PR-ML PR-FP PR-FN PR-IDs PR-FM PR-MOTA PR-MOTP PR-MOTAL
IOU All 32.34 12.88 20.93 7958.82 163739.85 4129.40 4221.89 35.77 40.81 36.48
KIOU All 37.4 7.3 21.5 8427.5 148393.9 422.7 605.4 39.0 40.7 39.1
KIOU 1/2 34.5 9.5 22.1 6803.5 155556 472.8 599.6 38.0 40.9 38.1
KIOU 1/3 31.9 10.9 23.4 7611.4 160566.9 483.2 630.9 37.0 40.9 37.1
KIOU 1/4 20.8 12.6 32.8 11163.4 192857.4 628.1 711.1 30.8 40.9 30.9

DETRAC-Test (Overall) Results

The reference results are taken from the UA-DETRAC results site. Only the best tracker / detector combination is displayed for each reference method.

Tracker Detector PR-MOTA PR-MOTP PR-MT PR-ML PR-IDs PR-FM PR-FP PR-FN Speed
CEM CompACT 5.1% 35.2% 3.0% 35.3% 267.9 352.3 12341.2 260390.4 4.62 fps
CMOT CompACT 12.6% 36.1% 16.1% 18.6% 285.3 1516.8 57885.9 167110.8 3.79 fps
GOG CompACT 14.2% 37.0% 13.9% 19.9% 3334.6 3172.4 32092.9 180183.8 390 fps
DCT R-CNN 11.7% 38.0% 10.1% 22.8% 758.7 742.9 336561.2 210855.6 0.71 fps
H2T CompACT 12.4% 35.7% 14.8% 19.4% 852.2 1117.2 51765.7 173899.8 3.02 fps
IHTLS CompACT 11.1% 36.8% 13.8% 19.9% 953.6 3556.9 53922.3 180422.3 19.79 fps
IOU R-CNN 16.0% 38.3% 13.8% 20.7% 5029.4 5795.7 22535.1 193041.9 100,840 fps
IOU EB 19.4% 28.9% 17.7% 18.4% 2311.3 2445.9 14796.5 171806.8 6,902 fps
KIOU EB 21.1% 28.6% 21.9% 17.6% 462.2 712.1 19046.8 159178.3 -
EB detections

These results are evaluated on detections of EB detector. We obtained our copy of detections from the authors of the original IOU Tracker.

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Python implementation of the Kalman-IOU Tracker

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