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openmht

Python module for multiple hypothesis tracking. Based on the article:

C. Kim, F. Li, A. Ciptadi and J. M. Rehg, "Multiple Hypothesis Tracking Revisited," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 4696-4704. doi: 10.1109/ICCV.2015.533 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7410890&isnumber=7410356

Note: This implementation utilizes motion scoring only (no appearance scoring)

Dependencies

Install the latest version of Python 3

Installation

$ pip install openmht

Input data

Format the input CSV columns with frame number and pixel positions using the examples under SampleData/ as a reference. The U,V values represent the 2D positions of objects/detections in that frame. A value of None in the output CSV indicates a missed detection. The Track column indicates the final track ID for a detection.

Parameters

Modify parameters by editing the file params.txt:

image_area: The image (frame) area in pixels (Default: 307200)

gating_area: Gating area for new detections (Default: 1000)

k: Gain or blending factor (Default: 0)

q: Kalman filter process variance (Default: 0.00001)

r: Estimate of measurement variance (Default: 0.01)

n: N-scan branch pruning parameter

Running:

OpenMHT takes 3 parameters: The input CSV, output CSV, and parameter file paths.

$ python -m openmht /Path/To/InputDetections.csv /Path/To/OutputDetections.csv /Path/To/ParameterFile.txt

Example output

InputOutput
Frame U V
0 -0.0411 0.208
0 9.97 10
0 15.2 14.9
1 14 13
1 -0.0099 0.00141
1 9.9 10.1
1 15.1 14.9
2 14.1 13
2 -0.009 0.00141
2 10 10.099
2 15 14.89
Frame Track U V
0 0 -0.0411 0.208
0 1 9.97 10.0
0 2 15.2 14.9
0 3 None None
1 0 -0.0099 0.00141
1 1 9.9 10.1
1 2 15.1 14.9
1 3 14.0 13.0
2 0 -0.009 0.00141
2 1 10.0 10.099
2 2 15.0 14.89
2 3 14.1 13.0

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