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 ID-switches and difficulties caused by long-term occlusion, cluttered background, and crowded scenes. We propose an approach to data association which incorporates both motion and appearance in a global manner. Unlike limited-temporal-locality methods which incorporate a few frames into the data association problem, we incorporate the whole temporal span and solve the data association problem for one object at a time, while implicitly incorporating the rest of the objects. In order to achieve this, we utilize Generalized Minimum Clique Graphs to solve the optimization problem of our data association method. Our proposed method yields a better formulated approach to data association which is supported by our superior results. Experiments show the proposed method makes significant improvements in tracking in the diverse sequences of Town Center, TUD-crossing, TUD-Stadtmitte, PETS2009, and a new sequence called Parking Lot compared to the state of the art methods.
Finding Tracklets Using GMCPThe process of generating tracklets using GMCP is demonstrated in the following video.
Tracking results on TUD-Crossing, Parking Lot and PETS2009 sequences are shown in the following video.
20-Minute presentation of the full paper by Amir R. Zamir
The tracking results for multiple sequences including PETS2009 and Parking Lot can be downloaded here. The folder also contains the updated ground truth for the Parking Lot sequence which includes annotations for long term occlusions.
The code can be downloaded here.
Parking Lot Sequence
The Parking Lot sequence can be downloaded here.
Amir Roshan Zamir, Afshin Dehghan, and Mubarak Shah, GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs, European Conference on Computer Vision (ECCV), 2012. [PDF], [BibTeX]
Afshin Dehghan, Haroon Idrees, Amir Roshan Zamir, and Mubarak Shah, (In alphabetical order) Keynote: Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities
In Proceedings of PED, June 2012, [PDF], [BibTeX]