An evolutionary optimized vehicle tracker in collaboration with a detection system

In this work a learning algorithm for visual object tracking is presented. As object representation a fast computable set of Haar-like features is used and a weighted correlation is applied for the matching process. The object tracker utilizes the same set of features that is already calculated for object detection and thus it is possible to reuse features for detection and tracking. The feature's weight values are optimized for the tracking purpose by means of evolutionary strategies. Different tests of the object tracker on real-world sequences are presented using vehicles as example objects. Additionally, an object detection system and the integration of the object tracker into that system is described. Besides the system is based on a cascade of boosted classifiers, Haar and Triangle features, an adaptive sliding window and finally a Kalman filter.