Adam Łuczak

Fast depth estimation on mobile platforms and FPGA devices

In this paper, we propose a fast technique for real-time depth estimation that is implementable on mobile devices like smartphones and tablets. Moreover, the FPGA-based implementations of this technique are also reported. The idea of this technique is to use small-block matching and exploit the recently estimated disparity values in order to enhance spatial consistency of the output disparity map. Using the Middlebury stereoscopic test images, the proposed real-time technique is compared to other techniques known from the references. The obtained results demonstrate high efficiency of the proposed technique implemented both on mobile platforms and FPGA devices.

A large barrel distortion in an acquisition system for multifocal images extraction

This paper presents a new way to use the well-known distortions introduced by the fisheye lens (ultra-wide-angle lenses) and a well-known algorithms for the correction of distortion in order to obtain completely new functionality in supervision and monitoring of video. The suggested way to use the barrel distortion will result in a smoother zooming and allows to achieve the multiple images simultaneously with the same resolution but with a different viewing angle. This feature may vary greatly increase the effectiveness of surveillance application by providing a greater amount of visual data from one camera.

Depth Map’s 2D Histogram Assisted Occlusion Handling in Video Object Tracking

The paper describes new algorithm for automatic video object tracking. Proposed architecture consists of two loops of Kalman filter. In the loop of the tracking process, the information achieved from video and from 2D histogram based on depth map is used. Two loops work simultanously and the parameters between the loops are interchanged when the occlusion occurs. The 2D histogram representation of the depth map has unique properties that can be used to improve the tracking eficiency especially in the case of occlusions of the objects in the image. Experimental results prove that the proposed system can accurately track multiple objects in complex scenes.