Unofficial implementation of the paper: Multiple People Tracking by Lifted Multicut and Person Re-identification
The software is developed using Ubuntu 16.04 and OSX with Python 3.5. The following libraries and tools are needed for this software to work correctly:
- tensorflow (1.x+)
- Keras (2.x+)
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Download the source code and its submodules using
git clone --recursive https://github.com/justayak/cabbage.git
When the above criterias are met a simple install routine can be called inside the source root
This script will create a text file called settings.txt. You will need this file when you are using the end-to-end algorithm.
Follow this steps to do an end-to-end run on a video:
import numpy as np from cabbage.MultiplePeopleTracking import execute_multiple_people_tracking video_name = 'the_video_name' X = np.zeros((n, h, w, 3)) # the whole video loaded as np array dmax = 100 Dt = np.zeros((m, 6)) # m=number of detections video_loc = '/path/to/video/imgs' # the video must be stored as a folder with the individual frames settings_loc = '/path/to/settings.txt' # generated by the install.sh script execute_multiple_people_tracking(video_loc, X, Dt, video_name, dmax, settings_loc) # after the program has finished you can find a text file at the settings.data_root location # called 'output.txt'. It is structured as follows: # id1, id2, 0 (has an edge) OR 1 (has no edge) # sample: # 0, 1, 0 # 0, 2, 0 # 0, 3, 1 # ... # the ids correspond with the positions of the first axis of the Dt-matrix
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 Tang, Siyu, et al. "Multiple people tracking by lifted multicut and person re-identification." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.