Integrated human target detection, identification and tracking for surveillance applications

In recent years, a large amount of research efforts have been spent in the tracking human targets using one or more visual sensors (cameras) in both indoor and outdoor security and surveillance environments such as airports, metro stations, etc. However, in majority, the problem of associating a reliable identification signature to a detected target when in motion, has often been complicated due to changing target appearance, changes in lighting conditions and also because of partial or full occlusion. Therefore control operators have always been engaged in supervising the process of tagging specific targets of interest. Although the complementary field of human target identification based on biometrics (particularly via face recognition) has been well researched and mature enough; not much efforts has been directed in combining these technologies in-order to make security and surveillance operations fully autonomous. In this paper, this integration of simultaneous detection, tracking, and face-recognition-based identification of human targets from a static camera is proposed. The accuracy, efficiency and robustness of the this proposed framework is assessed and illustrated over different standard datasets across a wide range of scenarios using appropriate performance metrics.