Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications

Efficient Depth Enhancement Using a Combination of Color and Depth Information

Studies on depth images containing three-dimensional information have been performed for many practical applications. However, the depth images acquired from depth sensors have inherent problems, such as missing values and noisy boundaries. These problems significantly affect the performance of applications that use a depth image as their input. This paper describes a depth enhancement algorithm based on a combination of color and depth information. To fill depth holes and recover object shapes, asynchronous cellular automata with neighborhood distance maps are used. Image segmentation and a weighted linear combination of spatial filtering algorithms are applied to extract object regions and fill disocclusion in the object regions. Experimental results on both real-world and public datasets show that the proposed method enhances the quality of the depth image with low computational complexity, outperforming conventional methods on a number of metrics. Furthermore, to verify the performance of the proposed method, we present stereoscopic images generated by the enhanced depth image to illustrate the improvement in quality.

Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in three-dimensional space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D (Red-Green-Blue and Depth) algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

Three-dimensional object motion and velocity estimation using a single computational RGB-D camera

In this paper, a three-dimensional (3D) object moving direction and velocity estimation method is presented using a dual off-axis color-filtered aperture (DCA)-based computational camera. Conventional object tracking methods provided only two-dimensional (2D) states of an object in the image for the target representation. The proposed method estimates depth information in the object region from a single DCA camera that transforms 2D spatial information into 3D model parameters of the object. We also present a calibration method of the DCA camera to estimate the entire set of camera parameters for a practical implementation. Experimental results show that the proposed DCA-based color and depth (RGB-D) camera can calculate the 3D object moving direction and velocity of a randomly moving object in a single-camera framework.