Moving Window Detector Research Articles

Moving Object Tracking Based on Sparse Optical Flow with Moving Window and Target Estimator.

Moving object detection and tracking are technologies applied to wide research fields including traffic monitoring and recognition of workers in surrounding heavy equipment environments. However, the conventional moving object detection methods have faced many problems such as much computing time, image noises, and disappearance of targets due to obstacles. In this paper, we introduce a new moving object detection and tracking algorithm based on the sparse optical flow for reducing computing time, removing noises and estimating the target efficiently. The developed algorithm maintains a variety of corner features with refreshed corner features, and the moving window detector is proposed to determine the feature points for tracking, based on the location history of the points. The performance of detecting moving objects is greatly improved through the moving window detector and the continuous target estimation. The memory-based estimator provides the capability to recall the location of corner features for a period of time, and it has an effect of tracking targets obscured by obstacles. The suggested approach was applied to real environments including various illumination (indoor and outdoor) conditions, a number of moving objects and obstacles, and the performance was evaluated on an embedded board (Raspberry pi4). The experimental results show that the proposed method maintains a high FPS (frame per seconds) and improves the accuracy performance, compared with the conventional optical flow methods and vision approaches such as Haar-like and Hog methods.

Motion-Adaptive Frame Deletion Detection for Digital Video Forensics

The detection of frame deletion forgery is of great significance in the field of video forensics. Existing approaches, however, are not applicable to video sequences with variable motion strengths. In addition, the impact of interfering frames has not been considered in these approaches. Our research aims to develop a motion-adaptive forensic method as well as to eliminate interfering frames. Through a study of the statistical characteristics of the most common interfering frames such as relocated I-frames, we develop a new fluctuation feature based on frame motion residuals to identify frame deletion points (FDPs). The fluctuation feature is further enhanced by an intra-prediction elimination procedure so that it can be adapted to sequences with various motion levels. The enhanced feature is measured using a moving window detector to identify the location of a FDP. Finally, a postprocessing procedure is proposed to eliminate the minor interferences of sudden lighting change, focus vibration, and frame jitter. Our experimental results demonstrate that for videos with variable motion strengths and different interfering frames, the true positive rate of the algorithm can reach 90% when the false alarm rate is 0.3%. Our proposed method could provide a foundation for many practical applications of video forensics.

TESTING FOR MULTIPLE BUBBLES: LIMIT THEORY OF REAL‐TIME DETECTORS

This article provides the limit theory of real‐time dating algorithms for bubble detection that were suggested in Phillips, Wu, and Yu (PWY; International Economic Review 52 [2011], 201–26) and in a companion paper by the present authors (Phillips, Shi, and Yu, 2015; PSY; International Economic Review 56 [2015a], 1099–1134. Bubbles are modeled using mildly explosive bubble episodes that are embedded within longer periods where the data evolve as a stochastic trend, thereby capturing normal market behavior as well as exuberance and collapse. Both the PWY and PSY estimates rely on recursive right‐tailed unit root tests (each with a different recursive algorithm) that may be used in real time to locate the origination and collapse dates of bubbles. Under certain explicit conditions, the moving window detector of PSY is shown to be a consistent dating algorithm even in the presence of multiple bubbles. The other algorithms are consistent detectors for bubbles early in the sample and, under stronger conditions, for subsequent bubbles in some cases. These asymptotic results and accompanying simulations guide the practical implementation of the procedures. They indicate that the PSY moving window detector is more reliable than the PWY strategy, sequential application of the PWY procedure, and the CUSUM procedure.

Testing for Multiple Bubbles: Limit Theory of Real Time Detectors

This paper provides the limit theory of real time dating algorithms for bubble detection that were suggested in Phillips, Wu and Yu (2011, PWY) and Phillips, Shi and Yu (2013b, PSY). Bubbles are modeled using mildly explosive bubble episodes that are embedded within longer periods where the data evolves as a stochastic trend, thereby capturing normal market behavior as well as exuberance and collapse. Both the PWY and PSY estimates rely on recursive right tailed unit root tests (each with a different recursive algorithm) that may be used in real time to locate the origination and collapse dates of bubbles. Under certain explicit conditions, the moving window detector of PSY is shown to be a consistent dating algorithm even in the presence of multiple bubbles. The other algorithms are consistent detectors for bubbles early in the sample and, under stronger conditions, for subsequent bubbles in some cases. These asymptotic results and accompanying simulations guide the practical implementation of the procedures. They indicate that the PSY moving window detector is more reliable than the PWY strategy, sequential application of the PWY procedure and the CUSUM procedure.

Evaluation of Collapsing Losses in Digital Radar Detection

The collapsing losses, due to multiple channel combinations, are evaluated for some digital radar detection schemes including the moving window detector.

The Detection Performance of a Modified Moving Window Detector

The modified moving window detector for binary integration has a shorter window length compared with the ordinary moving window detector because of the addition of a third threshold. When the second threshold is reached, a counter begins to count until the third threshold is reached. A graphical method using a probability chain is developed here to analyze and to calculate the detection performance of this type detector. The detection performances for some practical cases are calculated by computer and are compared with the ordinary moving window detector.

The DFTSD: A Sequential Suboptimum Processor for Multiple-Range-Bin Radar Systems

A class of simple and efficient digital sequential detectors is presented, which are particularly suitable for application to radar systems where the antenna dwell time is easily varied. A methematical method, based on the random walk model, is developed to evaluate the DFTSD (digital fixed-thresholds sequential detector) performance. The DFTSD has a loss of 0.2 to 0.3 dB with respect to the digital SPRT (sequential probability ratio test) in the case of a single-range-bin system. Finally, results for the multiple-range-bin case are presented and compared with the performance of a moving-window detector.

Angular Accuracy of a Scanning Radar Employing a Two-Pole Filter

A two-pole filter is proposed as a detector for a scanning radar. The optimum values of the filter coefficients are found and are approximated by a simple expression. The optimum two-pole filter requires a 0.15-dB increase in signal-to-noise ratio in order to provide the same detection capability as the optimum detector, and yields azimuth estimates whose standard deviation are within 15 percent of the Cramér-Rao lower bound. The estimator is simple to implement, avoiding the storage requirements of the moving window detector and the bias complications of the feedback integrator.

Performance of the Analog Moving Window Detector

A type of analog integrating moving window detector for use with a scanning pulse radar is examined. A performance analysis is carried out, which takes into account both the radiation pattern of the antenna and the dynamic character of the detection process due to the angular scanning of the antenna. An expression for the false alarm rate of the detector is first derived and evaluated numerically. The detection performance and angular accuracy are next determined in a direct Monte Carlo simulation of the detector on a digital computer for both no fading and pulse-to-pulse Rayleigh fading. Finally the influence on detection performance of the width used for the moving window is investigated.

A moving-window detector for binary integration

A device for performing binary integration on quantized data from a multiple-resolution-element radar is described. The moving window detector is described, and expressions for false-alarm and detection probabilities are given. A scheme for determining the optimum detection thresholds and the optimum quantizing levels is given. Application of the moving window detector to a particular multiple-range-element radar is described.