A Novel Framework to Localize Moving Targets in Video Surveillance Systems via Spectral Clustering

Accurately localizing moving targets in each individual frame of video clips captured via an ordinary surveillance system is still challenging nowadays, provided the fact that many serious problems, including sudden illumination changes, partial or full obstacles, rigid or non-rigid targets' shape transformations, etc., are still hard to be tackled at the current stage. In this study, a new semi-supervised offline incremental-learning framework via spectral clustering is introduced to solve the above moving target localization problem. The framework is composed of two steps. First, a computer-user interaction step is enabled on the first frame of a video clip, in order to allow the ending user to delineate a region-of-interested enclosing the interested target. Positive and negative samples are automatically determined via a stratified random sampling strategy therein. Second, a spatially-weighted metric-based measure is defined to reveal the similarity between pair-wise pixels. This similarity measure is then determined via a supervised spectral clustering technique algorithmically. The newly introduced framework is evaluated using a database composed of over 1000 frames, with comparisons towards 6 other well-established approaches for moving targets localization. Promising outcomes are demonstrated and the superiority of the new framework is suggested from the statistical point of view.