Temporal Differencing Research Articles

City subcenter as a regional development policy: Impact on the property market

AbstractThe aim of this study is to perform a locally specific investigation of a polycentric development policy through the lens of the property market. The capital city of China, Beijing, adopted a subcenter program for the purpose of developing a new economic growth pole in the suburbs through the redistribution of administrative and economic resources. Herein, we exploit spatial and temporal differencing in microproperty transaction data to address the issue of nonrandom selection of the site for developing the subcenter. The results show that in the first year after announcing the subcenter program, property prices in the subcenter rose by 10.5%, stabilizing at approximately 20% after 2 years of continuous increase. Moreover, property price growth decreases sharply with increased distance from the new administrative center in the subcenter, which will host tens of thousands of civil servants under the subcenter program. In response to the subcenter‐induced demand shock, locations with elastically supplied housing have experienced greater population growth and weaker appreciation in property prices. Although strict purchase restrictions have helped stabilize the subcenter's property market to some extent, the fever has spread to adjacent markets. The specific geography of property market dynamics provides an important channel to evaluate the impact of the subcenter program and offers insight regarding the development of tactics to optimize subcenter outcomes.

Comparison of optimized Markov Decision Process using Dynamic Programming and Temporal Differencing – A reinforcement learning approach

Reinforcement learning is one of the promising approaches for operations research problems. The incoming inspection process in any manufacturing plant aims to control quality, reduce manufacturing costs, eliminate scrap, and process failure downtimes due to non-conforming raw materials. Prediction of the raw material acceptance rate can regulate the raw material supplier selection and improve the manufacturing process by filtering out non-conformities. This paper presents a Markov model developed to estimate the probability of the raw material being accepted or rejected in an incoming inspection environment. The proposed forecasting model is further optimized for efficiency using the two reinforcement learning algorithms (dynamic programming and temporal differencing). The results of the two optimized models are compared, and the findings are discussed.

Statistical accuracy analysis of different detecting algorithms for surveillance system in smart city

<span>Several detecting algorithms are developed for real-time surveillance systems in the smart cities. The most popular algorithms due to its accuracy are: Temporal Differencing, Background Subtraction, and Gaussian Mixture Models. Selecting of which algorithm is the best to be used, based on accuracy, is a good choise, but is not the best. Statistical accuracy anlysis tests are required for achieving a confident decision. This paper presents further analysis of the accuracy by employing four parameters: false recognition, unrecognized, true recognition, and total fragmentation ratios. The results proof that no algorithm is selected as the perfect or suitable for all applications based on the total fragmentation ratio, whereas both false recognition ratio and unrecognized ratio parameters have a significant impact. The mlti-way Analysis of Variate (so-called K-way ANONVA) is used for proofing the results based on SPSS statistics.</span>

Place‐based policies, firm productivity, and displacement effects: Evidence from Shenzhen, China

AbstractDeveloping and transitional countries devote considerable funds to selected areas to stimulate local growth and firm productivity. We examine the impact of place‐based interventions due to the opening of science parks in Shenzhen, China, on firm productivity and factor use. Our identification strategy, exploiting spatial and temporal differencing in firm‐level data, addresses the issues that (a) the selection of science park locations is not random and (b) high‐productivity firms sort themselves into science parks. Firm productivity is approximately 15–25% higher due to the science park policy. The policy also increases local wages and leads to distortions due to job displacement.

Insight and inference for DVARS

Estimates of functional connectivity using resting state functional Magnetic Resonance Imaging (rs-fMRI) are acutely sensitive to artifacts and large scale nuisance variation. As a result much effort is dedicated to preprocessing rs-fMRI data and using diagnostic measures to identify bad scans. One such diagnostic measure is DVARS, the spatial root mean square of the data after temporal differencing. A limitation of DVARS however is the lack of concrete interpretation of the absolute values of DVARS, and finding a threshold to distinguish bad scans from good. In this work we describe a sum of squares decomposition of the entire 4D dataset that shows DVARS to be just one of three sources of variation we refer to as D-var (closely linked to DVARS), S-var and E-var. D-var and S-var partition the sum of squares at adjacent time points, while E-var accounts for edge effects; each can be used to make spatial and temporal summary diagnostic measures. Extending the partitioning to global (and non-global) signal leads to a rs-fMRI DSE table, which decomposes the total and global variability into fast (D-var), slow (S-var) and edge (E-var) components. We find expected values for each component under nominal models, showing how D-var (and thus DVARS) scales with overall variability and is diminished by temporal autocorrelation. Finally we propose a null sampling distribution for DVARS-squared and robust methods to estimate this null model, allowing computation of DVARS p-values. We propose that these diagnostic time series, images, p-values and DSE table will provide a succinct summary of the quality of a rs-fMRI dataset that will support comparisons of datasets over preprocessing steps and between subjects.

Vehicle detection and inter-vehicle distance estimation using single-lens video camera on urban/suburb roads

This paper presents a driver assistance system for vehicle detection and inter-vehicle distance estimation using a single-lens video camera on urban/suburb roads. The task of vehicle detection on urban/suburb roads is more challenging due to their high scene complexity. In this work, the still area of frame inside the host vehicle is first removed using temporal differencing, followed by detecting vanishing point. Segmentation of road regions is then conducted using vanishing point and road’s edge lines. Shadow regions at the bottoms of vehicles verified using the HOG feature and an SVM classifier are utilized to detect vehicle positions. The distances between the host and its front vehicles are estimated based on the locations of detected vehicles and vanishing point. Experimental results show varied performance of vehicle detection with different scenes of urban/suburb roads and the detection rate can achieve up to 94.08%, indicating the feasibility of the proposed method.

Human Action Recognition: A Template Matching-based Approach

The objective of this paper is to provide an analysis on detection and recognition of human motion. We focused on view-based spatio-temporal template matching. We have used background subtraction and temporal differencing by taking the required videos to extract the features and detect motion. Then Motion History Image (MHI)-based motion template is exploited to get the sequence of videos with dominant motion information. We present a Split-frame MHI concept in this paper. Histogram of Oriented Gradients (HOG) is used to describe the feature, which is extracted from an MHI template. These descriptors are trained and tested with Support Vector Machine (SVM) classifier. We have developed a new dataset of single human action at indoor environment to be named as AAMRZ. The accuracy of the strategy with well-known KTH action dataset of 6 classes is 86.6% and with AAMRZ motion dataset of 115 classes is 88%. Both the results are satisfactory based on the complexities of datasets.

Efficient Segmentation and Detection of Object in Complex and Crowded Scene

This paper intends to develop a technique to extract the moving object by using temporal differencing. Initially the stereo images are captured from vicon camera and the images are cropped as per the requirement. Next interested ROI is selected to extract the object is a particular scene. The noise that are present in the cropped images are removed with the help of filters. The objects are detected by considering height and occupancy of the object in a given scene. After obtaining the objects, morphological operations are applied and finally the objects are detected. It is applicable in verities of applications in real time.

Rotation Feature Extraction for Moving Targets Based on Temporal Differencing and Image Edge Detection

A rotation parameter extraction method based on temporal differencing and image edge detection from range-Doppler images is presented in this letter. The proposed method first detects the motion trail of the moving pixels caused by the rotating parts in temporal differential range-Doppler images using image edge detection. A Doppler-slow-time image is then generated from the edge pixels on the motion trail. Finally, the rotation parameters are extracted from the Doppler-slow-time image. The proposed method is simple, rapid, and practical. Computer simulations and experimental results demonstrate its effectiveness in terms of computation time compared with existing methods.

Robust tracking of multiple persons in real-time video

In this paper, we present a robust person tracking method that the particle swarm optimization (PSO) algorithm is used as the tracking strategy. The method is divided into two procedures: object/background segmentation and tracking. For object/background segmentation, we use the temporal differencing to detect the regions of interest. For tracking, the PSO algorithm is used for overcome the robustness problem in the high noisy background and multiple moving persons and/or under occlusion. The particles in PSO represent the position, width and height of the search window, and the fitness function is calculated by the distance of the color feature vector and the histogram intersection. When occluded, we add the motion vector plus the previous position of the tracking model. The particles fly around the search region to obtain an optimal match of the target. The experiments show that the proposed method can track the single person, multiple people even when occluded, and is more efficient and accurate than the conventional particle filter method.

18-4: A Low Latency Compression Algorithm for Visually Lossless Display Stream Systems Using Temporal Differencing

This paper describes a low latency compression algorithm intended for use in a visually lossless display stream system in order to improve the quality of still and slowly moving video. The motivation for the algorithm is to utilize a temporal differencing method to provide a low latency coding system that can attain better bits per pixel (bpp) compression than conventional approaches. This particular study observed that an algorithm operating well at 8 bpp without temporal differencing will show similar or better results at 6 bpp with the temporal differencing method. Conventional algorithms tend to grow exponentially in complexity as measured by silicon real estate (gates or line buffer memory, or both) to attain similar results. As complexity grows, so does power consumption. By employing temporal differencing, the inherent redundancy in low motion and still video can be employed to improve the quality of the video. Experimental results are summarized for a still image sequence and a variety of video sequences with varying amounts of motion and type (natural images/computer screen content). A sequence of identical still images benefits the most, with an average gain in PSNR of 8 dB using a test database of 20 different images.

Consistent Labeling Approach for a PTZ Camera Based on Template Cache and Least Recently Used Replacement Strategy

Object tracking is an important function of video surveillance system. For the same object in a multi-camera environment, how to assign the same label to this object is so-called consistent labeling problem. Many consistent labeling approaches proposed by previous studies mainly based on the environment with multiple fixed cameras. In this study, a consistent labeling approach is proposed for a PTZ (Pan-Tilt-Zoom) camera. The same object is assigned the same label while the object in the FOV (Field-of-View) of a PTZ camera without influencing by the pan/tilt rotation. In order to achieve the above goal, the proposed approach using several methods, such as temporal differencing, template matching, mean-shift tracking, Kalman filter, and so on. A template cache is also designed for preserving the templates of an object with various angles and a least recently used (LRU) replacement mechanism is used to update the cache. The experimental results show that accuracy of the proposed approach for the consistent labeling of a PTZ camera can reach about 83 percentage.

Background Subtraction in Dynamic Environment based on Modified Adaptive GMM with TTD for Moving Object Detection

Background subtraction is the first processing stage in video surveillance. It is a general term for a process which aims to separate foreground objects from a background. The goal is to construct and maintain a statistical representation of the scene that the camera sees. The output of background subtraction will be an input to a higher-level process. Background subtraction under dynamic environment in the video sequences is one such complex task. It is an important research topic in image analysis and computer vision domains. This work deals background modeling based on modified adaptive Gaussian mixture model (GMM) with three temporal differencing (TTD) method in dynamic environment. The results of background subtraction on several sequences in various testing environments show that the proposed method is efficient and robust for the dynamic environment and achieves good accuracy.

An Improved Moving Body Detection Method Based on Gaussian Mixture Model and Differencing

This paper presented a detecting algorithm based on improved Gaussian mixture model, which improved the speed of establishing and updating the model. Whilst, according to the detection requirements in different time periods, using the method of combining temporal differencing and background differencing to detect moving body improved the instantaneity and veracity of moving body detection.

A Traversing and Merging Algorithm of Blobs in Moving Object Detection

In the frame of surveillance video, the colour of both moving objects and background distribute randomly. Consequently, the foreground image obtained by background subtraction or temporal differencing always contains many discrete blobs, which are isolated but actually belong to the same object. These blobs influence the effi ciency of later process such as recognition, classification, and activity analysis. In order to organize the foreground blobs, a trav ersing and merging algorithm of blobs is proposed in this paper. The experimental results show that the proposed algorithm can merge blob effectively.

Algorithmic Analysis to Video Object Tracking and Background Segmentation and Wavelet Transform as New Approach to Video Object Tracking

Video object tracking and segmentation are the fundamental building blocks for smart surveillance system. Various algorithms like partial least square analysis, Markov model, Temporal differencing, background subtraction algorithm, adaptive background updating have been proposed but each were having drawbacks like object tracking problem, multibackground congestion, illumination changes, occlusion etc. The background segmentation worked on to principled object tracking by using two models Gaussian mixture model and level centre model. Wavelet transforms have been one of the important signal processing developments, especially for the applications such as time-frequency analysis, data compression, segmentation and vision. The key idea of the wavelet transform approach is to represents any arbitrary function f (t) as a superposition of a set of such wavelets or basis functions. Results show that algorithm performs well to remove occlusion and multibackground congestion as well as algorithm worked with removal of noise in the signals.

A Motion Region Detection and Tracking Method

Nowadays, video surveillance is indispensable in security-sensitive areas. Hence, a significant amount of work has been done in this field. This paper proposes a hybrid framework for motion region detection and an appearance-based real-time motion tracking system. Initially, a foreground map is extracted through a process of subtraction from a background model, applying a temporal differencing method. Then, shadow elimination and morphological operations are used to remove noise. Finally, models are initiated for each detected motion region by extracting features such as center of mass and a color correlogram, which are then used for tracking purposes. As the similarity in distances within a certain radius is measured, the probability of confusing objects is reduced considerably, and therefore, performance is optimized significantly. The proposed framework also uses a robust technique to label people within a group. This framework has the capability to work in indoor, semi-outdoor, and even outdoor environments that generate a penumbra shadow, and it handles the groups formed due to occlusion effectively. The framework takes good care of false foreground pixels due to penumbra shadow. Hence, the proposed framework will play a pivotal role in providing security in highly confidential areas.

Time dependent Schrödinger equation on arbitrary structured grids: Application to photoionization

A systematic technique for conservatively discretizing the time dependent Schrödinger equation on an arbitrary structured grid is given. Spatial differencing is carried out by finite volumes, and temporal differencing is carried out semi-implicitly. It is shown that the resulting algorithm conserves probability to within a round-off error regardless of the grid geometry. The algorithm is efficient for both serial and parallel computation. The conservative nature of the algorithm, and its phase accuracy, are demonstrated for a bound state, and for a free state in an electromagnetic field. The ionization rate for a hydrogen atom in a strong electromagnetic field is computed, and compared with the rate from tunneling theory. The regime of validity of tunneling theory is clarified.

Improved Algorithms for Motion Detection of Intelligent Video Surveillance System

Real-time segmentation of moving regions in image sequences is a fundamental step in video monitoring systems. This paper presents an improved motion detection algorithm in a dynamic scene based on change detection. The algorithm integrates the temporal differencing method and background subtraction method to achieve better performance. Background subtraction is a typical change detection approach to segment foreground, but the continuous or abrupt variations of lighting conditions that cause unexpected changes in intensities on the background reference image. So we combine the background subtraction’s result with temporal difference’s result. The foreground mask is segmented by both the methods of background subtraction and temporal differencing. Finally, a post-processing is applied on the obtained object mask to reduce regions and smooth the moving region boundary. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the variation of illumination, and the moving objects can be extracted effectively.

Video watermarking algorithm based on moving object detection

To improve the robustness of video watermarking,a video watermarking algorithm based on moving object detection was proposed.The paper made use of the temporal differencing algorithm to extract and sign the moving targets in the video image sequences,and then,achieved watermark embedding and extraction processes by Singular Value Decomposition(SVD) method.In the simulation,the Peak Signal-to-Noise Ratio(PSNR) was calculated to show that this scheme has a great invisibility and concealment,the strimark software was used to geometricly attack on watermarked image,the correlation coefficient was analyzed to prove that this algorithm has a great robustness.