321 research outputs found
Removing shadows from video
This paper presents a novel approach to automatic
shadow identification and removal from video input. Based on
the observation that the length and position of a shadow
changes linearly over a relatively long period in outdoor
environments, due to the relative movement of the sun, we can
distinguish a shadow from other dark regions in an input video.
Subsequently, we can identify the Reference Shadow as that
with the highest confidence of the aforementioned linear
changes. This Reference Shadow is used to fit the shadow-free
invariant model, with which the shadow-free invariant images
can be computed for all frames in the input video. Our method
does not require camera calibration and shadows from
stationary objects, as moving objects are detected
automatically
Object's shadow removal with removal validation
We introduce in this paper, a shadow detection and removal method for moving objects especially for humans and vehicles. An effective method is presented for detecting and removing shadows from foreground figures. We assume that the foreground figures have been extracted from the input image by some background subtraction method. A figure may contain only one moving object with or without shadow. The homogeneity property of shadows is explored in a novel way for shadow detection and image division technique is used. The process is followed by filtering, removal, boundary removal and removal validation
Robust Principal Component Analysis?
This paper is about a curious phenomenon. Suppose we have a data matrix,
which is the superposition of a low-rank component and a sparse component. Can
we recover each component individually? We prove that under some suitable
assumptions, it is possible to recover both the low-rank and the sparse
components exactly by solving a very convenient convex program called Principal
Component Pursuit; among all feasible decompositions, simply minimize a
weighted combination of the nuclear norm and of the L1 norm. This suggests the
possibility of a principled approach to robust principal component analysis
since our methodology and results assert that one can recover the principal
components of a data matrix even though a positive fraction of its entries are
arbitrarily corrupted. This extends to the situation where a fraction of the
entries are missing as well. We discuss an algorithm for solving this
optimization problem, and present applications in the area of video
surveillance, where our methodology allows for the detection of objects in a
cluttered background, and in the area of face recognition, where it offers a
principled way of removing shadows and specularities in images of faces
Drone Shadow Tracking
Aerial videos taken by a drone not too far above the surface may contain the
drone's shadow projected on the scene. This deteriorates the aesthetic quality
of videos. With the presence of other shadows, shadow removal cannot be
directly applied, and the shadow of the drone must be tracked. Tracking a
drone's shadow in a video is, however, challenging. The varying size, shape,
change of orientation and drone altitude pose difficulties. The shadow can also
easily disappear over dark areas. However, a shadow has specific properties
that can be leveraged, besides its geometric shape. In this paper, we
incorporate knowledge of the shadow's physical properties, in the form of
shadow detection masks, into a correlation-based tracking algorithm. We capture
a test set of aerial videos taken with different settings and compare our
results to those of a state-of-the-art tracking algorithm.Comment: 5 pages, 4 figure
Detecting and Shadows in the HSV Color Space Using Dynamic Thresholds
The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise. Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds
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