Real-Time Event-Triggered Object Tracking in the Presence of Model Drift and Occlusion

In this paper, we propose a novel event-triggered tracking framework for fast and robust visual tracking in the presence of model drift and occlusion. The resulting tracker not only operates in real time, but also is resilient to tracking failures caused by factors such as fast motion and heavy occlusion. Specifically, the tracker consists of an event-triggered decision model as the core module that coordinates other functional modules, including a short-term tracker, occlusion and drift identification, target redetection, short-term tracker updating, and online discriminative learning for a detector. Each functional module is associated with a defined event that is triggered when the proposed conditions are met. The occlusion and drift identification module is intended to perform online evaluation of the short-term tracking. When a model drift event occurs, the target redetection module is activated by the event-triggered decision model to relocate the target and reinitialize the short-term tracker. The short-term tracker updating is carried out at each frame with a variable learning rate depending on the degree of occlusion. A sampling pool is constructed to store discriminative samples that are used to update the detector model. Extensive experiments on large benchmark datasets demonstrate that the proposed tracking algorithm can effectively detect model drift and restore tracking, and more importantly, it outperforms the state-of-the-art approaches in terms of accuracy, efficiency, and robustness.