631 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
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Video content analysis for automated detection and tracking of humans in CCTV surveillance applications
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented
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
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Salient object detection increasingly receives attention as an important
component or step in several pattern recognition and image processing tasks.
Although a variety of powerful saliency models have been intensively proposed,
they usually involve heavy feature (or model) engineering based on priors (or
assumptions) about the properties of objects and backgrounds. Inspired by the
effectiveness of recently developed feature learning, we provide a novel Deep
Image Saliency Computing (DISC) framework for fine-grained image saliency
computing. In particular, we model the image saliency from both the coarse- and
fine-level observations, and utilize the deep convolutional neural network
(CNN) to learn the saliency representation in a progressive manner.
Specifically, our saliency model is built upon two stacked CNNs. The first CNN
generates a coarse-level saliency map by taking the overall image as the input,
roughly identifying saliency regions in the global context. Furthermore, we
integrate superpixel-based local context information in the first CNN to refine
the coarse-level saliency map. Guided by the coarse saliency map, the second
CNN focuses on the local context to produce fine-grained and accurate saliency
map while preserving object details. For a testing image, the two CNNs
collaboratively conduct the saliency computing in one shot. Our DISC framework
is capable of uniformly highlighting the objects-of-interest from complex
background while preserving well object details. Extensive experiments on
several standard benchmarks suggest that DISC outperforms other
state-of-the-art methods and it also generalizes well across datasets without
additional training. The executable version of DISC is available online:
http://vision.sysu.edu.cn/projects/DISC.Comment: This manuscript is the accepted version for IEEE Transactions on
Neural Networks and Learning Systems (T-NNLS), 201
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