81 research outputs found
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Moving Object Detection using Adaptive Blind Update and RGB-D Camera
A novel background subtraction approach using RGB-D camera and an adaptive blind updating policy is introduced. This method in initialization creates a model to store background pixels to compare each pixel of the new frame with the model in the same location to identify background pixels. The background-model update presented in this paper uses regular and blind update which also has a different criteria from existing methods. In particular, blind update frequently changes based on the background changes and the speed of moving object. This will allow the scene model to adapt to the changes in the background, detecting the stationary moving object and reducing the ghost phenomenon. In addition, proposed bootstrapping segmentation and shadow detection are added to the system to improve the accuracy of the algorithm in shadow and depth camouflage scenarios. The proposed method is compared with the original method and other state of the art algorithms. Experimental results show significant improvement in those videos that stationary object appear. In addition, the benchmark results also indicate strong and stable results compared to the other state of the art algorithms.10.13039/501100007914-Brunel University London
RGB-D datasets using microsoft kinect or similar sensors: a survey
RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms
Deep Attention Models for Human Tracking Using RGBD
Visual tracking performance has long been limited by the lack of better appearance models. These models fail either where they tend to change rapidly, like in motion-based tracking, or where accurate information of the object may not be available, like in color camouflage (where background and foreground colors are similar). This paper proposes a robust, adaptive appearance model which works accurately in situations of color camouflage, even in the presence of complex natural objects. The proposed model includes depth as an additional feature in a hierarchical modular neural framework for online object tracking. The model adapts to the confusing appearance by identifying the stable property of depth between the target and the surrounding object(s). The depth complements the existing RGB features in scenarios when RGB features fail to adapt, hence becoming unstable over a long duration of time. The parameters of the model are learned efficiently in the Deep network, which consists of three modules: (1) The spatial attention layer, which discards the majority of the background by selecting a region containing the object of interest; (2) the appearance attention layer, which extracts appearance and spatial information about the tracked object; and (3) the state estimation layer, which enables the framework to predict future object appearance and location. Three different models were trained and tested to analyze the effect of depth along with RGB information. Also, a model is proposed to utilize only depth as a standalone input for tracking purposes. The proposed models were also evaluated in real-time using KinectV2 and showed very promising results. The results of our proposed network structures and their comparison with the state-of-the-art RGB tracking model demonstrate that adding depth significantly improves the accuracy of tracking in a more challenging environment (i.e., cluttered and camouflaged environments). Furthermore, the results of depth-based models showed that depth data can provide enough information for accurate tracking, even without RGB information
Context-driven Object Detection and Segmentation with Auxiliary Information
One fundamental problem in computer vision and robotics is to
localize objects of interest in an image. The task can either be
formulated as an object detection problem if the objects are
described by a set of pose parameters, or an object segmentation
one if we recover object boundary precisely. A key issue in
object detection and segmentation concerns exploiting the spatial
context, as local evidence is often insufficient to determine
object pose in the presence of heavy occlusions or large object
appearance variations. This thesis addresses the object detection
and segmentation problem in such adverse conditions with
auxiliary depth data provided by RGBD cameras. We focus on four
main issues in context-aware object detection and segmentation:
1) what are the effective context representations? 2) how can we
work with limited and imperfect depth data? 3) how to design
depth-aware features and integrate depth cues into conventional
visual inference tasks? 4) how to make use of unlabeled data to
relax the labeling requirements for training data?
We discuss three object detection and segmentation scenarios
based on varying amounts of available auxiliary information. In
the first case, depth data are available for model training but
not available for testing. We propose a structured Hough voting
method for detecting objects with heavy occlusion in indoor
environments, in which we extend the Hough hypothesis space to
include both the object's location, and its visibility pattern.
We design a new score function that accumulates votes for object
detection and occlusion prediction. In addition, we explore the
correlation between objects and their environment, building a
depth-encoded object-context model based on RGBD data. In the
second case, we address the problem of localizing glass objects
with noisy and incomplete depth data. Our method integrates the
intensity and depth information from a single view point, and
builds a Markov Random Field that predicts glass boundary and
region jointly. In addition, we propose a nonparametric,
data-driven label transfer scheme for local glass boundary
estimation. A weighted voting scheme based on a joint feature
manifold is adopted to integrate depth and appearance cues, and
we learn a distance metric on the depth-encoded feature manifold.
In the third case, we make use of unlabeled data to relax the
annotation requirements for object detection and segmentation,
and propose a novel data-dependent margin distribution learning
criterion for boosting, which utilizes the intrinsic geometric
structure of datasets. One key aspect of this method is that it
can seamlessly incorporate unlabeled data by including a graph
Laplacian regularizer. We demonstrate the performance of our
models and compare with baseline methods on several real-world
object detection and segmentation tasks, including indoor object
detection, glass object segmentation and foreground segmentation
in video
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Nonparametric background modelling and segmentation to detect Micro Air Vehicles (MAV) using RGB-D Sensor
A novel approach to detect Micro Air Vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving MAVs by combining colour and depth information. The proposed method stores several colour and depth images as a model and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background-model update presented uses different criteria from existing methods. Additionally, a blind-update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different MAVs and publicly available datasets. Results showing some improvements over existing methods are discussed
ImageSpirit: Verbal Guided Image Parsing
Humans describe images in terms of nouns and adjectives while algorithms
operate on images represented as sets of pixels. Bridging this gap between how
humans would like to access images versus their typical representation is the
goal of image parsing, which involves assigning object and attribute labels to
pixel. In this paper we propose treating nouns as object labels and adjectives
as visual attribute labels. This allows us to formulate the image parsing
problem as one of jointly estimating per-pixel object and attribute labels from
a set of training images. We propose an efficient (interactive time) solution.
Using the extracted labels as handles, our system empowers a user to verbally
refine the results. This enables hands-free parsing of an image into pixel-wise
object/attribute labels that correspond to human semantics. Verbally selecting
objects of interests enables a novel and natural interaction modality that can
possibly be used to interact with new generation devices (e.g. smart phones,
Google Glass, living room devices). We demonstrate our system on a large number
of real-world images with varying complexity. To help understand the tradeoffs
compared to traditional mouse based interactions, results are reported for both
a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit
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