Object of interest extraction in low-frame-rate image sequences and application to mobile mapping systems

Here, we present a novel object of interest (OOI) extraction framework designed for low-frame-rate (LFR) image sequences, typically from mobile mapping systems (MMS). The proposed method integrates tracking and segmentation in a unified framework. We propose a novel object-shaped kernel-based scale-invariant mean shift algorithm to track the OOI through the LFR sequences and keep the temporal consistency. Then the well-known GrabCut approach for static image segmentation is generalized to the LFR sequences. We analyze the imaging geometry of the OOI in LFR sequences collected by the MMS and design a Kalman filter module to assist the proposed tracker. Extensive experimental results on real LFR sequences collected by VISAT (TM) MMS demonstrate that the proposed approach is robust to the challenges such as low frame rate, fast scaling, and large inter-frame displacement of the OOI. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.OE.51.6.067201]National Natural Science Foundation of China [40971245