Image change detectio algorithms : A systematic survey

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

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Image change detection algorithms: a systematic survey

A new probabilistic background model based on a Hidden Markov Model is presented. The hidden states of the model enable discrimination between foreground, background and shadow. This model functions as a low level process for a car tracker. A particle filter is employed as a stochastic filter for the car tracker. The use of a particle filter allows the incorporation of the information from the low level process via importance sampling. A novel observation density for the particle filter which models the statistical dependence of neighboring pixels based on a Markov random field is presented. The effectiveness of both the low level process and the observation likelihood are demonstrated.

A probabilistic background model for tracking

We propose a novel approach to understanding activities from their partial observations monitored through multiple non-overlapping cameras separated by unknown time gaps. In our approach, each camera view is first decomposed automatically into regions based on the correlation of object dynamics across different spatial locations in all camera views. A new Cross Canonical Correlation Analysis (xCCA) is then formulated to discover and quantify the time delayed correlations of regional activities observed within and across multiple camera views in a single common reference space. We show that learning the time delayed activity correlations offers important contextual information for (i) spatial and temporal topology inference of a camera network; (ii) robust person re-identification and (iii) global activity interpretation and video temporal segmentation. Crucially, in contrast to conventional methods, our approach does not rely on either intra-camera or inter-camera object tracking; it thus can be applied to low-quality surveillance videos featured with severe inter-object occlusions. The effectiveness and robustness of our approach are demonstrated through experiments on 330 hours of videos captured from 17 cameras installed at two busy underground stations with complex and diverse scenes.

/pdf/time-delayed-correlation-analysis-for-multi-camera-activity-p70zf0clkg.pdf

Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding

An interactive display system configured for detecting an object or user input provided with an object. The system includes a display surface on which graphic images are displayed, one or more scanning light sources configured for scanning the interactive display surface, and a light detector configured for detecting light reflected from an object that is adjacent to or in contact with the interactive display surface. A computing system storing machine instructions is in communication with the scanning light source and the light detector. When executed, the machine instructions cause the computing system to illuminate the interactive display surface with the scanning light source, to detect light with the light detector that is reflected from an object after illumination with the light source, and to generate an output signal based on the detected light that has been reflected from an object on or adjacent to the interactive display surface.

Input method for surface of interactive display

Sudden illumination change detection using order consistency

A system and method for object detection with sudden illumination changes provides a system including a camera model unit for computing a difference map between a first input image and a second input image, an illumination model unit in signal communication with the camera model unit for comparing the sign of the difference between two pixels in a first input image against the sign of the difference between two pixels in a second input image, and a CPU in signal communication with the camera model unit and the illumination model unit for detecting a changed object in accordance with said comparison when said sign of the difference is changed; and a corresponding method including providing a first image having a number of pixels, receiving a second image subsequent to the first image, calculating a difference map between the first and second images, selecting a pair of pixel locations from the first or second images in accordance with the difference map, statistically modeling a camera noise level for the first and second images, computing the likelihood of order inconsistency between a pair of pixel values at the selected locations in the first image and a pair of pixel values at the selected locations in the second image in accordance with the camera noise level; and detecting a changed object in accordance with the computed likelihood.

Object detection for sudden illumination changes using order consistency

Automatic face recognition has a lot of application areas and current single-camera face recognition has severe limitations when the subject is not cooperative, or there are pose changes and different illumination conditions. A face recognition system using multiple cameras overcomes these limitations. In each channel, real-time component-based face detection detects the face with moderate pose and illumination changes employing fusion of individual component detectors for eyes and mouth, and the normalized face is recognized using an LDA recognizer. A reliability measure is trained using the features extracted from both face detection and recognition processes, to evaluate the inherent quality of channel recognition. The recognition from the most reliable channel is selected as the final recognition results. The recognition rate is far better than that of either single channel, and consistently better than common classifier fusion rules

/pdf/multi-camera-face-recognition-by-reliability-based-selection-5askzze4wx.pdf

Multi-Camera Face Recognition by Reliability-Based Selection

Single-camera face recognition has severe limitations when the subject is not cooperative, or there are pose changes and different illumination conditions. Face recognition using multiple synchronized cameras is proposed to overcome the limitations. We introduce a reliability measure trained from examples to evaluate the inherent quality of channel recognition. The recognition from the channel predicted to be the most reliable is selected as the final recognition results. In this paper, we enhance Adaboost to improve the component based face detector running in each channel as well as the channel reliability measure training. Effective features are designed to train the channel reliability measure using data from both face detection and recognition. The recognition rate is far better than that of either single channel, and consistently better than common classifier fusion rules

/pdf/on-channel-reliability-measure-training-for-multi-camera-5b4b5ajmo5.pdf

Binglong Xie

Papers

Sudden illumination change detection using order consistency

Object detection for sudden illumination changes using order consistency

Multi-Camera Face Recognition by Reliability-Based Selection

On Channel Reliability Measure Training for Multi-Camera Face Recognition