Multiple object tracking based on motion segmentation of point trajectories

In this paper we propose an algorithm for multiple object tracking, a heavily researched but still challenging problem of computer vision. We follow the tracking by detection paradigm in an online fashion and formulate tracking as a typical assignment problem between detections and existing tracks that is solved by a modification of the Hungarian algorithm. Contrary to other methods that use a multitude of features based on appearance, optical flow and prior knowledge gained through training, we solely use clusters of point trajectories to link detections and tracks. Point trajectories are robust under partial occlusions and allow the expansion of a track even in the absence of a detection. At the core of our algorithm lies a motion segmentation method that extracts coherent clusters from triangulated point trajectories. Our algorithm achieves competitive results on the 2D MOT 2015 benchmark showcasing its potential.