459 research outputs found
Real-time object detection using monocular vision for low-cost automotive sensing systems
This work addresses the problem of real-time object detection in automotive environments
using monocular vision. The focus is on real-time feature detection,
tracking, depth estimation using monocular vision and finally, object detection by
fusing visual saliency and depth information.
Firstly, a novel feature detection approach is proposed for extracting stable and
dense features even in images with very low signal-to-noise ratio. This methodology
is based on image gradients, which are redefined to take account of noise as
part of their mathematical model. Each gradient is based on a vector connecting a
negative to a positive intensity centroid, where both centroids are symmetric about
the centre of the area for which the gradient is calculated. Multiple gradient vectors
define a feature with its strength being proportional to the underlying gradient
vector magnitude. The evaluation of the Dense Gradient Features (DeGraF) shows
superior performance over other contemporary detectors in terms of keypoint density,
tracking accuracy, illumination invariance, rotation invariance, noise resistance
and detection time.
The DeGraF features form the basis for two new approaches that perform dense
3D reconstruction from a single vehicle-mounted camera. The first approach tracks
DeGraF features in real-time while performing image stabilisation with minimal
computational cost. This means that despite camera vibration the algorithm can
accurately predict the real-world coordinates of each image pixel in real-time by comparing
each motion-vector to the ego-motion vector of the vehicle. The performance
of this approach has been compared to different 3D reconstruction methods in order
to determine their accuracy, depth-map density, noise-resistance and computational
complexity. The second approach proposes the use of local frequency analysis of
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gradient features for estimating relative depth. This novel method is based on the
fact that DeGraF gradients can accurately measure local image variance with subpixel
accuracy. It is shown that the local frequency by which the centroid oscillates
around the gradient window centre is proportional to the depth of each gradient
centroid in the real world. The lower computational complexity of this methodology
comes at the expense of depth map accuracy as the camera velocity increases, but
it is at least five times faster than the other evaluated approaches.
This work also proposes a novel technique for deriving visual saliency maps by
using Division of Gaussians (DIVoG). In this context, saliency maps express the
difference of each image pixel is to its surrounding pixels across multiple pyramid
levels. This approach is shown to be both fast and accurate when evaluated against
other state-of-the-art approaches. Subsequently, the saliency information is combined
with depth information to identify salient regions close to the host vehicle.
The fused map allows faster detection of high-risk areas where obstacles are likely
to exist. As a result, existing object detection algorithms, such as the Histogram of
Oriented Gradients (HOG) can execute at least five times faster.
In conclusion, through a step-wise approach computationally-expensive algorithms
have been optimised or replaced by novel methodologies to produce a fast object
detection system that is aligned to the requirements of the automotive domain
Real-time statistical saliency using high throughput circuit design and its applications in psychophysical study
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 89).Using low level video data, features can be extracted from images to predict search time and statistical saliency in a way that models the human visual system. The statistical saliency model helps explain how visual search and attention systems direct eye movement when presented with an image. The statistical saliency of a target object is defined as distance in feature space of the target to its distractors. This thesis presents a real-time, full through-put, parallel processing implementation design for the statistical saliency model, utilizing the stability and parallelization of programmable circuits. Discussed are experiments in which real-time saliency analysis suggests the addition of temporal features. The goal of this research is to achieve accurate saliency predictions at real-time speed and provide a framework for temporal and motion saliency. Applications for real-time statistical saliency include live analysis in saliency research, guided visual processing tasks, and automated safety mechanisms for use in automobiles.by David A. Blau.M.Eng
A space-variant visual pathway model for data efficient deep learning
We present an investigation into adopting a model of the retino-cortical mapping, found in biological visual systems, to improve the efficiency of image analysis using Deep Convolutional Neural Nets (DCNNs) in the context of robot vision and egocentric perception systems. This work has now enabled DCNNs to process input images approaching one million pixels in size, in real time, using only consumer grade graphics processor (GPU) hardware in a single pass of the DCNN
Unified Image and Video Saliency Modeling
Visual saliency modeling for images and videos is treated as two independent
tasks in recent computer vision literature. While image saliency modeling is a
well-studied problem and progress on benchmarks like SALICON and MIT300 is
slowing, video saliency models have shown rapid gains on the recent DHF1K
benchmark. Here, we take a step back and ask: Can image and video saliency
modeling be approached via a unified model, with mutual benefit? We identify
different sources of domain shift between image and video saliency data and
between different video saliency datasets as a key challenge for effective
joint modelling. To address this we propose four novel domain adaptation
techniques - Domain-Adaptive Priors, Domain-Adaptive Fusion, Domain-Adaptive
Smoothing and Bypass-RNN - in addition to an improved formulation of learned
Gaussian priors. We integrate these techniques into a simple and lightweight
encoder-RNN-decoder-style network, UNISAL, and train it jointly with image and
video saliency data. We evaluate our method on the video saliency datasets
DHF1K, Hollywood-2 and UCF-Sports, and the image saliency datasets SALICON and
MIT300. With one set of parameters, UNISAL achieves state-of-the-art
performance on all video saliency datasets and is on par with the
state-of-the-art for image saliency datasets, despite faster runtime and a 5 to
20-fold smaller model size compared to all competing deep methods. We provide
retrospective analyses and ablation studies which confirm the importance of the
domain shift modeling. The code is available at
https://github.com/rdroste/unisalComment: Presented at the European Conference on Computer Vision (ECCV) 2020.
R. Droste and J. Jiao contributed equally to this work. v3: Updated Fig. 5a)
and added new MTI300 benchmark results to supp. materia
Object and feature based modelling of attention in meeting and surveillance videos
MPhilThe aim of the thesis is to create and validate models of visual attention. To
this extent, a novel unsupervised object detection and tracking framework has been
developed by the author. It is demonstrated on people, faces and moving objects
and the output is integrated in modelling of visual attention. The proposed approach
integrates several types of modules in initialisation, target estimation and validation.
Tracking is rst used to introduce high-level features, by extending a popular model
based on low-level features[1]. Two automatic models of visual attention are further
implemented. One based on winner take it all and inhibition of return as the mech-
anisms of selection on a saliency model with high- and low-level features combined.
Another which is based only on high-level object tracking results and statistic proper-
ties from the collected eye-traces, with the possibility of activating inhibition of return
as an additional mechanism. The parameters of the tracking framework thoroughly
investigated and its success demonstrated. Eye-tracking experiments show that high-
level features are much better at explaining the allocation of attention by the subjects
in the study. Low-level features alone do correlate signi cantly with real allocation
of attention. However, in fact it lowers the correlation score when combined with
high-level features in comparison to using high-level features alone. Further, ndings
in collected eye-traces are studied with qualitative method, mainly to discover direc-
tions in future research in the area. Similarities and dissimilarities between automatic
models of attention and collected eye-traces are discusse
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