Efficient Global Multi-object Tracking Under Minimum-cost Circulation Framework

GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs

Data association is an essential component of any human tracking system. The majority of current methods, such as bipartite matching, incorporate a limited-temporal-locality of the sequence into the data association problem, which makes them inherently prone to IDswitches and difficulties caused by long-term occlusion, cluttered background, and crowded scenes. We propose an approach to data ass...

Multi-Object Tracking Based on Tracking-Learning-Detection Framework

This paper shows the framework of robust long-term and real-time tracking of multi-object under dynamic background. Here multi-object means either the same types or totally different types. For each tracked object a classifier corresponding to the object is trained on-line using positive and negative constraints inside local region. An external Detector module is integrated into the framework t...

Efficient Multiple People Tracking Using Minimum Cost Arborescences

We present a new global optimization approach for multiple people tracking based on a hierarchical tracklet framework. A new type of tracklets is introduced, which we call tree tracklets. They contain bifurcations to naturally deal with ambiguous tracking situations. Difficult decisions are postponed to a later iteration of the hierarchical framework, when more information is available. We cast...

A strongly polynomial minimum cost circulation algorithm

On Pairwise Cost for Multi-Object Network Flow Tracking

Multi-object tracking has been recently approached with the min-cost network flow optimization techniques. Such methods simultaneously resolve multiple object tracks in a video and enable modeling of dependencies among tracks. Min-cost network flow methods also fit well within the “tracking-by-detection” paradigm where object trajectories are obtained by connecting per-frame outputs of an objec...