Selective subtraction when the scene cannot be learned

Abstract

Background subtraction techniques model the background of the scene using the stationarity property and classify the scene into two classes of foreground and background. In doing so, most moving objects become foreground indiscriminately, except for perhaps some waving tree leaves, water ripples, or a water fountain, which are typically "learned" as part of the background using a large training set of video data. We introduce a novel concept of background as the objects other than the foreground, which may include moving objects in the scene that cannot be learned from a training set because they occur only irregularly and sporadically, e.g. a walking person. We propose a "selective subtraction" method as an alternative to standard background subtraction, and show that a reference plane in a scene viewed by two cameras can be used as the decision boundary between foreground and background. In our definition, the foreground may actually occur behind a moving object. Furthermore, the reference plane can be selected in a very flexible manner, using for example the actual moving objects in the scene, if needed. We present diverse set of examples to show that: (i) the technique performs better than standard background subtraction techniques without the need for training, camera calibration, disparity map estimation, or special camera configurations; (ii) it is potentially more powerful than standard methods because of its flexibility of making it possible to select in real-time what to filter out as background, regardless of whether the object is moving or not, or whether it is a rare event or a frequent one.

Selective Subtraction for Handheld Cameras

“…For instance, in Figure 2 when an object moves, the rate of change of τ can be estimated and can be used in several applications including vehicle navigation or detecting anomalies in pedestrian paths. Furthermore, the idea of a single reference plane and the estimation of projective depth can be extended to the use of multiple reference planes which would allows us to classify a scene as layers of foreground or background [37] where different objects could belong to different foreground layers. The ability of having multiple reference planes enables us to define multiple foregrounds or multiple backgrounds and hence a notion of in-between two layers.…”

A2Ba: Adaptive Background Modelling for Visual Aerial Surveillance Conditions

“…Above can be achieved by being invariant or by adapting to changes. These changes can be local, small persisting moving objects belonging to images like waving trees or shadows [5][6], or global, like illumination changes or camera motion [7] [8]. Spatial and temporal information of pixels are two fundamental elements to understand background structure and these should complement each other to obtain a robust background model [9].…”

Background subtraction for aerial surveillance conditions

“…Above can be achieved by being invariant or by adapting to changes. These can be local, small persisting moving objects belonging to images like waving trees [5] [6], or global, like illumination changes or camera motion [7] [8]. Spatial and temporal information of pixels are two fundamental elements to understand background structure and these should complement each other to obtain a robust background model [9].…”