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.