Spatially Adaptive Subsampling for Motion Detection

Many video surveillance applications rely on efficient motion detection. However, the algorithms are usually costly since they compute a background model at every pixel of the frame. This paper shows that, in the case of a planar scene with a fixed calibrated camera, a set of pixels can be selected to compute the background model while ignoring the other pixels for accurate but less costly motion detection. The cali- bration is used to first define a volume of interest in the real world and to project the volume of interest onto the image, and to define a spatial adaptive subsampling of this region of interest with a subsampling density that depends on the camera distance. Indeed, farther objects need to be analyzed with more precision than closer objects. Tests on many video sequences have integrated this adaptive subsampling to various motion detection techniques.

Steganalysis of MSU Stego Video Based on Block Matching of Interframe Collusion and Motion Detection

MSU Stego Video is a public video steganographic tool, which has strong robustness and is regarded as a real video steganographic tool. In order to increase the detection rate, this paper proposes a new steganoalysis method against MSU, which uses the chessboard character of MSU embedded video, proposes a down-sample block-based collusion method to estimate the original frame and checks the chessboard mode of the different frame between tested frame and estimated frame to detect MSU steganographic evidences. To reduce the error introduced by severe movement of the video content, a method that abandons severe motion blocks from detecting is proposed. The experiment results show that the false negative rate of the proposed algorithm is lower than 5%, and the false positive rate is lower than 2%. Our algorithm has significantly better performance than existing algorithms. Especially to the video that has fast motion, the algorithm has more remarkable performance.

Facile and Economical Fabrication of Superhydrophobic Flexible Resistive Strain Sensors for Human Motion Detection

Superhydrophobic flexible strain sensors have great application value in the fields of personal health monitoring,human motion detection,and soft robotics due to their good flexibility and high sensitivity.However,complicated preparation processes and costly processing procedures have limited their development.To overcome these limitations,in this work we develop a facile and low-cost method for fabricating superhydrophobic flexible strain sensor via spraying carbon black(CB)nanoparticles dispersed in a thermoplastic elastomer(SEBS)solution on a polydimethylsiloxane(PDMS)flexible substrate.The prepared strain sensor had a large water contact angle of 153±2.83°and a small rolling angle of 8.5±1.04°,and exhibited excellent self-cleaning property.Due to the excellent superhydrophobicity,aqueous acid,salt,and alkali could quickly roll off the flexible strain sensor.In addition,the sensor showed excellent sensitivity(gauge factor(GF)of 5.4–7.35),wide sensing ranges(stretching:over 70%),good linearity(three linear regions),low hysteresis(hysteresis error of 4.8%),and a stable response over 100 stretching-releasing cycles.Moreover,the sensor was also capable of effectively detecting human motion signals like finger bending and wrist bending,showing promising application prospects in wearable electronic devices,personalized health monitoring,etc.

ACCURATE DETECTION OF HIGH-SPEED MULTI-TARGET VIDEO SEQUENCES MOTION REGIONS BASED ON RECONSTRUCTED BACKGROUND DIFFERENCE

The paper first discusses shortcomings of classical adjacent-frame difference. Sec ondly, based on the image energy and high order statistic(HOS) theory, background reconstruction constraints are setup. Under the help of block-processing technology, background is reconstructed quickly. Finally, background difference is used to detect motion regions instead of adjacent frame difference. The DSP based platform tests indicate the background can be recovered losslessly in about one second, and moving regions are not influenced by moving target speeds. The algorithm has important usage both in theory and applications.

IMAGE OBJECT DETECTION BASED ON FRACTIONAL BROWNIAN MOTION

Fractional Brownian motion, continuous everywhere and differentiable nowhere, offers a convenient modeling for irregular nonstationary stochastic processes with long-term dependencies and power law behavior of spectrum over wide ranges of frequencies. It shows high correlation at coarse scale and varies slightly at fine scale, which is suitable for and successful in describing and modeling natural scenes. On the other hand, man-made objects can be constructively well described by using a set of regular simple shape primitives such as line, cylinder, etc. and are free of fractal. Based on the difference, a method to discriminate man-made objects from natural scenes is provided. Experiments are used to demonstrate the good efficiency of developed technique.

Suitable triggering algorithms for detecting strong ground motions using MEMS accelerometers

With the recent development of digital Micro Electro Mechanical System （MEMS） sensors, the cost of monitoring and detecting seismic events in real time can be greatly reduced. Ability of MEMS accelerograph to record a seismic event depends upon the efficiency of triggering algorithm, apart from the sensor＇s sensitivity. There are several classic triggering algorithms developed to detect seismic events, ranging from basic amplitude threshold to more sophisticated pattern recognition. Algorithms based on STA/LTA are reported to be computationally efficient for real time monitoring. In this paper, we analyzed several STA/LTA algorithms to check their efficiency and suitability using data obtained from the Quake Catcher Network （network of MEMS accelerometer stations）. We found that most of the STA/LTA algorithms are suitable for use with MEMS accelerometer data to accurately detect seismic events. However, the efficiency of any particular algorithm is found to be dependent on the parameter set used （i.e., window width of STA, LTA and threshold level）.

Automatic Anomaly Detection of Respiratory Motion Based on Singular Spectrum Analysis

The realization of automatic anomaly detection of respiratory motion could be very useful to prevent accidental damage during radiation therapy. In this paper, we proposed an automatic anomaly detection method using singular value decomposition analysis. Before applying this method, the investigator needs a normal respiratory motion data of a patient. From these data, a trajectory matrix representing normal time-series feature is created. Decomposing the matrix, we obtained the feature of normal time series. Then, we applied the same procedure to real-time data and obtained real-time features. Calculating the similarity of those feature matrixes, an anomaly score was obtained. Patient motion was observed by a depth camera. In our simulation, two types of motion e.g. cough and sudden stop of breathing were successfully detected, while gradual change of respiratory cycle frequency was not detected clearly.

A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment

In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space is used to extract pre-detected regions instead of traditional motion differential method, as it’s more suitable for fire detection in indoor environment. Secondly, according to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established. Thirdly,a multi-expert system consisting of color component dispersion,similarity and centroid motion is established to identify flames.The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment.

Moving Objects Detection Using Intersecting Cortical Model in Enhanced Fish-eye Image

A new method of the moving objects detection using the enhanced fish-eye lens and the intersecting cortical model （ICM） algorithm is proposed. The improved fish-eye lens is designed through controlling the entrance pupils of the lens. This lens has an ultra field of view about 183 degrees, and can image an ellipse picture on the 4 ： 3 rectangular CCD surface, which increases the CCD utilization and the image resolution. The ICM is a model based on pulse coupled neural network（PCNN） which is espeeially designed for image processing. It is derived from several visual cortex models and is basically the intersection of these models. The theoretical foundation of the ICM is given. An improved ICM algorithm in which some parameters are modified is used to detect moving objects specially. The experiment indicated that moving objects can be detected reliably and efficiently using ICM algorithm from the elliptical fish-eye image. It can be used in the field of traffic monitoring and other security domains.

Fuzzy Logic Recursive Gaussian Denoising of Color Video Sequences via Shot Change Detection

A new technique using fuzzy in a recursive fashion is presented to deal with the Gaussian noise. In this technique, the keyframes and between frames are identified initially and the keyframe is denoised efficiently. This frame is compared with the between frames to remove noise. To do so the frames are partitioned into blocks;the motion vector is calculated;also the difference is measured using the dissimilarity function. If the blocks have no motion vectors in the block, the block of value is copied to the between frames otherwise the difference between the blocks is calculated and this value is filtered with temporal filtering. The blocks are processed in overlapping manner to avoid the blocking effect and also to reduce the additional edges created while processing. The simulation results show that the peak signal to noise ratio of the new technique is improved up to 1 dB and also the execution time is greatly reduced.

Motion Tracking with Fast Adaptive Background Subtraction

To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction algorithm and the motion tracking process using this algorithm. The algorithm uses only luminance components of sampled image sequence pixels and models every pixel in a statistical model. The algorithm is characterized by its ability of real time detecting sudden lighting changes, and extracting and tracking motion objects faster. It is shown that our algorithm can be realized with lower time and space complexity and adjustable object detection error rate with comparison to other background subtraction algorithms. Making use of the algorithm, an indoor monitoring system is also worked out and the motion tracking process is presented in this paper. Experimental results testify the algorithm's good performances when used in an indoor monitoring system.

Detecting Objectionable Videos

This paper addresses the problem of detecting objectionable videos, which has never been carefully studied before. Our method can be efficiently used to filter objectionable videos on Internet. One tensor based key-frame selection algorithm, one cube based color model and one objectionable video estimation algorithm are presented. The key frame selection is based on motion analysis using the three-dimensional structure tensor. Then the cube based color model is employed to detect skin color in each key frame. Finally, the video estimation algorithm is applied to estimate objectionable degree in videos. Experimental results on a variety of real-world videos downloaded from Internet show that this method is promising.

MOTION-BASED REGION GROWING SEGMENTATION OF IMAGE SEQUENCES

This paper proposes a motion-based region growing segmentation scheme for the object-based video coding, which segments an image into homogeneous regions characterized by a coherent motion. It adopts a block matching algorithm to estimate motion vectors and uses morphological tools such as open-close by reconstruction and the region-growing version of the watershed algorithm for spatial segmentation to improve the temporal segmentation. In order to determine the reliable motion vectors, this paper also proposes a change detection algorithm and a multi-candidate pro- screening motion estimation method. Preliminary simulation results demonstrate that the proposed scheme is feasible. The main advantage of the scheme is its low computational load.

利用InSAR形变反演滑坡滑面运动特征——以汶川县布瓦村滑坡为例

当前滑坡滑面运动特征获取多采用接触式手段,费时费力、成本高昂,且仅能获取稀疏点位滑面运动状态。因此,本文提出一种非接触式滑坡滑面运动特征获取方法,其基本思路是:基于位错理论建立滑坡表面形变与滑面运动间的关系模型,同时引入滑面运动平滑约束条件,最终以InSAR等技术获取的地表形变为约束,反演获得滑面运动特征。本文选择四川省汶川县布瓦村滑坡作为研究对象,以时序InSAR技术获取的滑坡坡面升降轨形变数据为约束,基于发展的理论方法反演获得布瓦村滑坡滑面几何与运动学参数,并结合野外勘查、Lidar观测以及模型残差较好地验证了反演结果的可靠性,其中反演升降轨模型残差仅3.1 mm·a^(-1)和3.4 mm·a^(-1)。反演结果显示,布瓦村滑坡滑面位于坡表以下平均~15.0 m深度处,滑面整体以沿坡向运动为主,最大运动量级达~80 mm·a^(-1),同时在坡体西侧中下部发现微量的垂直坡向运动。本文提出方法可方便、快速的获取空间连续的滑坡滑面运动特征,为滑坡灾害预警和失稳风险评估提供更为直接可靠的科学参考。

面向人体运动检测的水凝胶基柔性应变传感器设计

在可穿戴电子设备迅猛发展的背景下,构建面向人体运动检测的可穿戴柔性应变传感器成为目前传感器研究的重点之一。基于聚丙烯酰胺(PAM)、纤维素纳米晶(CNCs)、单宁酸(TA)构建了一种导电水凝胶基柔性应变传感器。机械性能与电学性能测试结果表明,相较于其他柔性应变传感器,聚丙烯酰胺-纤维素纳米晶-单宁酸导电水凝胶基柔性应变传感器具有超长的拉伸范围(0%~2 900%)、较好的黏附性、快速的响应时间(280 ms)和良好的稳定性(超320次加载-卸载循环)。该传感器对人体关节弯曲的大应变与喉部活动的小应变等运动信号具备良好的检测功能,可用作人体运动检测的柔性可穿戴传感器。

基于动态视觉传感器的无人机目标检测与避障

针对无人机在动态环境中感知动态目标与躲避高速动态障碍物,提出了基于动态视觉传感器的目标检测与避障算法。设计了滤波方法和运动补偿算法,滤除事件流中背景噪声、热点噪声及由相机自身运动产生的冗余事件;设计了一种融合事件图像和RGB图像的动态目标融合检测算法,保证检测的可靠性。根据检测结果对目标运动轨迹进行估计,结合障碍物运动特点和无人机动力学约束改进速度障碍法躲避动态障碍物。大量仿真试验、手持试验及飞行试验验证了所提算法的可行性。

模拟复眼视叶神经网的目标运动方向检测模型

如何对杂乱背景中物体(目标)的运动方向做出准确可靠的检测与感知,是计算机视觉研究领域中一个重要问题。自然界中,飞虫(如苍蝇、蜻蜓等)高适应性和高可靠性的感知目标运动是一种自然特性,本文基于飞虫−果蝇视叶神经纤维网最新的生理学研究成果,提出一种基于果蝇视觉感知目标运动方向的多层级检测模型系统。通过对不同场景下拍摄的视频序列样本进行实验和测试,并与2-Q运动检测器模型、基于ON和OFF信号通道处理运动信息的检测模型等进行了对比,验证了其在杂乱背景下对于目标水平和垂直方向运动检测的有效性和鲁棒性。

TARGET DETECTION USING MULTI-RESOLUTION ANALYSIS AND HOLDER CONSTANT

Through analyzing the different height parameter of 3D surface between the artificial target and complex background based on the description of average Holder constant of fractional Brownian motion, a novel method of target detection based on wavelet transformation and Holder constant is proposed. The wavelet Holder constants are calculated and linearly interpolated in a series of images, the target is detected by testing the linearity errof The more accurate localization can be achieved using two images of the same region but with difIerent scaling parameters.The application results of this algorithm for target detection are also given, and show that this method has good performance of noise immunity. This method is also suitable for identifying specific targets in complex background.