In background modeling and motion detection module, they employ an adaptive model for background estimation applying mixture of Gaussians and appearance patterns, thereby presenting a stable and robust background model.
In order to perform adaptive motion detection, background modeling has been chosen because of its computationally efficient implementation.
In order to cope with outdoor environments, our framework aims at developing a novel Color-based Independent Comodelnt Analysis (CICA) model for motion detection in a color-image sequence.
Here, we present a neural network model of motion detection, figure ground segregation and attentive selection which explains these response patterns in an unifying framework.
The four component modules described in this paper are (i) motion detection using a layered background model, (ii) object tracking based on local appearance, (iii) hierarchical object recognition, and (iv) fused multisensor object tracking using multiple features and geometric constraints.
The approach makes use of three strategies found in biological systems: space-variant sensing, a spatio-temporal-frequency-based model of motion detection and the alignment of sensory-motor maps.
For this purpose, we extended the model for motion detection by a mechanism of temporal integration [24].
Moreover, textured stimuli were based on a simple forward model of motion detection (Leon et al. 2012).
Our segmentation method is an extension of standard motion detection methods with adaptive background modeling.
In this section, some detection methods that include parts such as background modeling and motion detection will be reviewed.
Classical models of motion detection rely on non-linear interactions between two inputs after different temporal filtering.
