7. Conclusions and future works

This chapter provides a step-by-step tutorial for creating an accurate and high-performance tracking-by-detection algorithm out of ordinary detectors, by eliciting an effective collaboration among them. The use of active learning in junction with co-learning enables the creation of a battery of tracker that strives to minimize the uncertainty of one classifier by the help of another. The progressive design leads to use a committee of classifiers that use online bagging to keep up with the latest target appearance changes while improving the accuracy and generalization of the base tracker (a feature-based KNN). Inspired by the query-by-bagging algorithm, this

algorithm selects the most informative samples to learn from the long-term memory auxiliary detector, which realizes a gradually decreasing dependence on this slow and likely overfit detector yet robust against fluctuations in target appearance and occlusions. Furthermore, using an expectation of the bounding boxes compensates for overreliance of the tracker on the classifiers' confidence function. The balance in stability-plasticity equilibrium is achieved by the combination of several short-term classifiers with a long-term classifier and managing their interaction with an active learning mechanism.

The trail of proposed trackers led to T6, which incorporates ensemble tracking, active learning, and co-learning in a discriminative tracking framework and outperform state-of-the-art discriminative and generative trackers on a large video dataset with various types of challenges such as appearance changes and occlusions.

The future direction of this study involves other detectors to care for context, to have accurate physical models for known categories, to use deep features to improve discrimination, and to examine different methods of building the ensemble and detecting most informative samples or exchanging.
