Spatio-temporal Attention Mechanism for More Complex Analysis to Track Multiple Objects

Abstract

This paper proposes the spatio-temporal attentive mechanism to track multiple objects, even occluded objects. The proposed system provides an efficient method for more complex analysis using data association in spatially attentive window and predicted temporal location. When multiple objects are moving or occluded between them in areas of visual field, a simultaneous tracking of multiple objects tends to fail. This is due to the fact that incompletely estimated feature vectors such as location, color, velocity, and acceleration of a target provide ambiguous and missing information. In addition, partial information cannot render the complete information unless temporal consistency is considered when objects are occluded between them or they are hidden in obstacles. Thus, the spatially and temporally considered mechanism using occlusion activity detection and object association with partial probability model is proposed. For an experimental evaluation, the proposed algorithms are applied to real image sequences. Experimental results in a natural environment demonstrate the usefulness of the proposed method.