Date

2017-04-01

Author

YİĞİT, Ahmet
Temizel, Alptekin

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Tracking groups of people is a challenging problem. Groups may grow or shrink dynamically with merging and splitting of individuals and conventional trackers are not designed to handle such cases. In this study, the authors present a conjoint individual and group tracking (CIGT) framework based on particle filter and online learning. CIGT has four complementary phases: two-phase association, false positive elimination, tracking and learning. First, reliable tracklets are created and detection responses are associated to tracklets in two-phase association. Then, hierarchal false positive elimination is performed for unassociated detection responses. In the tracking phase, CIGT calculates multiple weights from the observation and jointly models individuals and groups. Particle advection is used in the motion model of CIGT to facilitate tracking of dense groups. In the learning phase, the discriminative appearance model, consisting of shape, colour and texture features, is extracted and used in AdaBoost online learning. Using the discriminative learning model, state estimation is performed on both individuals and groups. The experimental results show that the performance of the proposed framework compares favourably with other individual and group-tracking methods for both real and synthetic datasets.

Subject Keywords

Object tracking, Particle filtering (numerical methods), Learning (artificial intelligence), Object detection, Feature extraction, Image colour analysis, Image texture, Image motion analysis, Image filtering, State estimation, Social interaction evaluation, Conjoint individual and group tracking framework, CIGT framework, Particle filter, Online learning phase, Two-phase association, False positive elimination, Reliable tracklets, Detection responses, Hierarchal false positive elimination, Unassociated detection response, Particle advection, CIGT motion model, AdaBoost online learning, Discriminative appearance model, Texture feature extraction, Colour feature extraction, Shape feature extraction, Discriminative learning model, State estimation

URI

https://hdl.handle.net/11511/29927

Collections

Graduate School of Informatics, Article