10,239 research outputs found
Available seat counting in public rail transport
Surveillance cameras are found almost everywhere today, including vehicles for public transport. A lot of research has already been done on video analysis in open spaces. However, the conditions in a vehicle for public transport differ from these in open spaces, as described in detail in this paper. A use case described in this paper is on counting the available seats in a vehicle using surveillance cameras. We propose an algorithm based on Laplace edge detection, combined with background subtraction
Scene modelling using an adaptive mixture of Gaussians in colour and space
We present an integrated pixel segmentation and region
tracking algorithm, designed for indoor environments. Visual monitoring systems often use frame differencing techniques to independently classify each image pixel as either foreground or background. Typically, this level of processing does not take account of the global image structure, resulting in frequent misclassification.
We use an adaptive Gaussian mixture model in colour and space to represent background and foreground regions of the scene. This model is used to probabilistically classify observed pixel values, incorporating the global scene structure into pixel-level segmentation. We evaluate our system over 4 sequences and show that it successfully segments foreground pixels and tracks major foreground regions as they move through the scene
A Nonconvex Projection Method for Robust PCA
Robust principal component analysis (RPCA) is a well-studied problem with the
goal of decomposing a matrix into the sum of low-rank and sparse components. In
this paper, we propose a nonconvex feasibility reformulation of RPCA problem
and apply an alternating projection method to solve it. To the best of our
knowledge, we are the first to propose a method that solves RPCA problem
without considering any objective function, convex relaxation, or surrogate
convex constraints. We demonstrate through extensive numerical experiments on a
variety of applications, including shadow removal, background estimation, face
detection, and galaxy evolution, that our approach matches and often
significantly outperforms current state-of-the-art in various ways.Comment: In the proceedings of Thirty-Third AAAI Conference on Artificial
Intelligence (AAAI-19
Embedded Line Scan Image Sensors: The Low Cost Alternative for High Speed Imaging
In this paper we propose a low-cost high-speed imaging line scan system. We
replace an expensive industrial line scan camera and illumination with a
custom-built set-up of cheap off-the-shelf components, yielding a measurement
system with comparative quality while costing about 20 times less. We use a
low-cost linear (1D) image sensor, cheap optics including a LED-based or
LASER-based lighting and an embedded platform to process the images. A
step-by-step method to design such a custom high speed imaging system and
select proper components is proposed. Simulations allowing to predict the final
image quality to be obtained by the set-up has been developed. Finally, we
applied our method in a lab, closely representing the real-life cases. Our
results shows that our simulations are very accurate and that our low-cost line
scan set-up acquired image quality compared to the high-end commercial vision
system, for a fraction of the price.Comment: 2015 International Conference on Image Processing Theory, Tools and
Applications (IPTA
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