This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.

/pdf/vibe-a-universal-background-subtraction-algorithm-for-video-3hbi5ckurx.pdf

ViBe: A Universal Background Subtraction Algorithm for Video Sequences

Efficient adaptive density estimation per image pixel for the task of background subtraction

Over the last decade, the availability of public image repositories and recognition benchmarks has enabled rapid progress in visual object category and instance detection. Today we are witnessing the birth of a new generation of sensing technologies capable of providing high quality synchronized videos of both color and depth, the RGB-D (Kinect-style) camera. With its advanced sensing capabilities and the potential for mass adoption, this technology represents an opportunity to dramatically increase robotic object recognition, manipulation, navigation, and interaction capabilities. In this paper, we introduce a large-scale, hierarchical multi-view object dataset collected using an RGB-D camera. The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology. This paper describes the dataset collection procedure and introduces techniques for RGB-D based object recognition and detection, demonstrating that combining color and depth information substantially improves quality of results.

/pdf/a-large-scale-hierarchical-multi-view-rgb-d-object-dataset-4eoda5ra0f.pdf

A large-scale hierarchical multi-view RGB-D object dataset

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.

A texture-based method for modeling the background and detecting moving objects

Real-time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated visual surveillance, human-machine interface, and very low-bandwidth telecommunications A typical method is background subtraction Many background models have been introduced to deal with different problems One of the successful solutions to these problems is to use a multi-colour background model per pixel proposed by Grimson et al [1, 2,3] However, the method suffers from slow learning at the beginning, especially in busy environments In addition, it can not distinguish between moving shadows and moving objects This paper presents a method which improves this adaptive background mixture model By reinvestigating the update equations, we utilise different equations at different phases This allows our system learn faster and more accurately as well as adapts effectively to changing environment A shadow detection scheme is also introduced in this paper It is based on a computational colour space that makes use of our background model A comparison has been made between the two algorithms The results show the speed of learning and the accuracy of the model using our update algorithm over the Grimson et al’s tracker When incorporate with the shadow detection, our method results in far better segmentation than The Thirteenth Conference on Uncertainty in Artificial Intelligence that of Grimson et al

/pdf/an-improved-adaptive-background-mixture-model-for-real-time-1l517cez5l.pdf

An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection

A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes

Investigation of the relationship between internal fluid leakage through a valve and the acoustic emission generated from the leakage

In this paper, four main types of corrosion: uniform, pitting, crevice and stress corrosion cracking (SCC) found in the petrochemical industry, were characterized and identified by Acoustic Emission (AE) analysis using their locations and extracted AE parameters. A novel low-cost AE location system based on a Field Programmable Gate Array PC (FPGA-PC) and a LOCAN 320 AE analyzer were utilized in this study. Specimens used in experiments were austenitic stainless-steel SS304. The pattern of AE signals from each type of corrosion was plotted using their location and correlation. Experimental results show the ability of our FPGA-PC system to determine corrosion locations. The correlations of AE parameters including amplitude, counts, hits and time were used to identify different types of corrosion. In addition, the characteristics of the corrosion process for each type are explained using the AE signals obtained corresponding to the source locations, together with experimental observation.

Pakorn Kaewtrakulpong

Papers

An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection

A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes

Investigation of the relationship between internal fluid leakage through a valve and the acoustic emission generated from the leakage

Study on source location using an acoustic emission system for various corrosion types

Smart portable noninvasive instrument for detection of internal air leakage of a valve using acoustic emission signals