A pixel-level, intensity-based nonlinear autoregressive classifier (NARX) with chromatic exogenous input for efficient image background subtraction
Syed Adnan Yusuf, David J Brown, Alan Mackinnon, Richard Papanicolaou · 2013
Background subtraction is a well-known technique in computer vision to extract foreground objects from background reference frames. In real-time video processing applications such as surveillance, behavioral profiling and intelligent transport systems, the domain presents a number of challenges. Video frames used to train such models contain a range of dynamic background activities such as waving trees, moving cloud cover or abrupt intensity variations that make the foreground detection a challenging task. Dynamic neural networks are known for their capability to predict time-series-based nonlinear models via previous feature data. The proposed scenario models each pixel's intensity/color-alternating behavior based on its previous activity patterns. Any significant or unusual variation in the underlying intensity or color value therefore is modeled as a foreground activity. Based on this concept, this paper presents a non-linear autoregressive neural (BG-NARX) classifier with the pixels' chromatic values as the exogenous vectors to improve background detection accuracy. The proposed model was evaluated against three benchmarking video datasets and reported promising detection accuracies ranging from 67-94% for pedestrians and vehicles against highly variable backgrounds with low false positives and negatives.