Center for Research in Comptuer Vision
Center for Research in Comptuer Vision



Target Identity-aware Network Flow for Online Multiple Target Tracking



Introduction



In this paper we show that multiple object tracking (MOT) can be formulated in a framework, where the detection and data-association are performed simultaneously. Our method allows us to overcome the confinements of data association based MOT approaches; where the performance is dependent on the object detection results provided at input level. At the core of our method lies structured learning which learns a model for each target and infers the best location of all targets simultaneously in a video clip. The inference of our structured learning is done through a new Target Identity-aware Network Flow (TINF), where each node in the network encodes the probability of each target identity belonging to that node. The proposed Lagrangian relaxation optimization finds the high quality solution to the network. During optimization a soft spatial constraint is enforced between the nodes of the graph which helps reducing the ambiguity caused by nearby targets with similar appearance in crowded scenarios. We show that automatically detecting and tracking targets in a single framework can help resolve the ambiguities due to frequent occlusion and heavy articulation of targets. Our experiments involve challenging yet distinct datasets and show that our method can achieve results better than the state-of-art.

Pipeline




TINF vs Network flow



Full Presentation

20-Minute presentation of the full paper by Afshin Dehghan


Tracking Output and Groundtruth

Since the bounding box size is set manually and it might be inaccurate, we used 30% overlap threshold in all of our experiments.

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Parking Lot 1:
Tracking Output
Groundtruth

Parking Lot 2:
Tracking Output
Groundtruth

Parking Lot Pizza:
Tracking Output
Groundtruth

Running:
Tracking Output
Groundtruth

Dancing:
Tracking Output
Groundtruth

Code

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Data


Dancing
Running

PowerPoint

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Poster

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Related Publications

Afshin Dehghan, Yicong Tian, Philip. H. S. Torr and Mubarak Shah, Target Identity-aware Network Flow for Online Multiple Target Tracking IEEE International Conference on Computer Vision and Pattern Recognition, 2015. [PDF], [BibTeX]

Afshin Dehghan, Shayan Modiri Assari and Mubarak Shah, GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking IEEE International Conference on Computer Vision and Pattern Recognition, 2015. [PDF], [BibTeX]

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]

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