An Innovative SIFT-Based Method for Rigid Video Object Recognition

This paper presents an innovative SIFT-based method for rigid video object recognition (hereafter called RVO-SIFT). Just like what happens in the vision system of human being, this method makes the object recognition and feature updating process organically unify together, using both trajectory and feature matching, and thereby it can learn new features not only in the training stage but also in the recognition stage, which can improve greatly the completeness of the video object’s features automatically and, in turn, increases the ratio of correct recognition drastically. The experimental results on real video sequences demonstrate its surprising robustness and efficiency

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

Video object extraction via MRF-based contour tracking

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