Author details

T6. Interestingly, it is observed that in most of the subcategories that T6 is clearly better than the other trackers, the success plot of T6 starts with a plateau and later has a sharp drop around τov ¼ 0:8. This means that T6 provides high-quality localization (i.e., bigger overlaps with the ground truth). Similarly, from precision plot, it is evident that T6 shows a graceful degradation in different scenarios, and although it does not provide a good scale adaptation

Figure 8. Qualitative results of T6 in red against other trackers (T0–T5 in blue and TLD, STRK, CSK, MIL, and BSBT in gray) on challenging video scenarios of OTB-100 [65]. The sequences are (from top to bottom, left to right) FaceOcc2 and Walking2 with severe occlusion, Deer and Skating1 with abrupt motions, Firl and Ironman with drastic rotations, Singer1 and CarDark and Shaking with poor lighting, Jumping and Basketball with nonrigid deformations, and Shaking,Soccer with drastic lighting, pose, and noise level changes and Board with intensive background clutter. The ground truth is

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

for targets, it is able to localize them better than the competing trackers (Figure 8).

illustrated with yellow dashed box. The results are available in http://ishiilab.jp/member/meshgi-k/act.html.

7. Conclusions and future works

118 Human-Robot Interaction - Theory and Application

Kourosh Meshgi\* and Shigeyuki Oba

\*Address all correspondence to: meshgi-k@sys.i.kyoto-u.ac.jp

Graduate School of Informatics, Kyoto University, Kyoto, Japan
