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.

Anisotropic,Wrinkled,and Crack-Bridging Structure for Ultrasensitive,Highly Selective Multidirectional Strain Sensors

Flexible multidirectional strain sensors are crucial to accurately determining the complex strain states involved in emerging sensing applications.Although considerable efforts have been made to construct anisotropic structures for improved selective sensing capabilities,existing anisotropic sensors suffer from a trade-off between high sensitivity and high stretchability with acceptable linearity.Here,an ultrasensitive,highly selective multidirectional sensor is developed by rational design of functionally different anisotropic layers.The bilayer sensor consists of an aligned carbon nanotube(CNT)array assembled on top of a periodically wrinkled and cracked CNT-graphene oxide film.The transversely aligned CNT layer bridge the underlying longitudinal microcracks to effectively discourage their propagation even when highly stretched,leading to superior sensitivity with a gauge factor of 287.6 across a broad linear working range of up to 100%strain.The wrinkles generated through a pre-straining/releasing routine in the direction transverse to CNT alignment is responsible for exceptional selectivity of 6.3,to the benefit of accurate detection of loading directions by the multidirectional sensor.This work proposes a unique approach to leveraging the inherent merits of two cross-influential anisotropic structures to resolve the trade-off among sensitivity,selectivity,and stretchability,demonstrating promising applications in full-range,multi-axis human motion detection for wearable electronics and smart robotics.

Bidirectional Background Modeling for Video Surveillance

Traditional background model methods often require complicated computations, and are sensitive to illumination and shadow. In this paper, we propose a block-based background modeling method, and use our proposed method to combine color and texture characteristics. Suppression and relaxation are the two key strategies to resist illumination changes and shadow disturbance. The proposed method is quite efficient and is capable of resisting illumination changes. Experimental results show that our method is suitable for real-word scenes and real-time applications.

Measuring ground deformations caused by 2015 Mw7.8 Nepal earthquake using high-rate GPS data

The April 25, 2015 Mw7.8 Nepal earthquake was successfully recorded by Crustal Movement Observation Network of China （CMONOC） and Nepal Geodetic Array （NGA）. We processed the high-rate GPS data （1 Hz and 5 Hz） by using relative kinematic positioning and derived dynamic ground motions caused by this large earthquake. The dynamic displacements time series clearly indicated the displacement amplitude of each station was related to the rupture directivity. The stations which located in the di- rection of rupture propagation had larger displacement amplitudes than others. Also dynamic ground displacement exceeding 5 cm was detected by the GPS station that was 2000 km away from the epicenter. Permanent coseismic displacements were resolved from the near-field high-rate GPS stations with wavelet decomposition-reconstruction method and P-wave arrivals were also detected with S transform method. The results of this study can be used for earthquake rupture process and Earthquake Early Warning studies.

Coded aperture compressive imaging array applied for surveillance systems

This paper proposes an application of compressive imaging systems to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system and a corresponding motion target detection algorithm in video using compressive image data are developed. Coded masks with random Gaussian, Toeplitz and random binary are utilized to simulate the compressive image respectively. For compressive images, a mixture of the Gaussian distribution is applied to the compressed image field to model the background. A simple threshold test in compressive sampling image is used to declare motion objects. Foreground image retrieval from underdetermined measurement using the total variance optimization algorithm is explored. The signal-to-noise ratio （SNR） is employed to evaluate the image quality recovered from the compressive sampling signals, and receiver operation characteristic （ROC） curves are used to quantify the performance of the motion detection algorithm. Experimental results demonstrate that the low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz mask, motion detection algorithms using the random binary phase mask can yield better detection results. However using the random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed images.

Identifying Periodical Activities Independent of Video Content

In this research, a completely new and accurate method has been presented for detecting periodic activities with the help of machine vision. The proposed method is independent of motion tracking complex algorithms unlike the previous strategies and it is fully independent of contents and types of activities by performing low level calculation. Not using of heavy computations while improving the ability of periodicity detection is regarded as the unique feature of this method. The use of general and flexible framework in this method causes to facilitate the machine vision periodic activities identification process.

Flexible Strain Sensor Based on 3D Electrospun Carbonized Sponge

Flexible strain sensor has attracted much attention because of its potential application in human motion detection.In this work,the prepared strain sensor was obtained by encapsulating electrospun carbonized sponge(CS)with room temperature vulcanized silicone rubber(RTVS).In this paper,the formation mechanism of conductive sponge was studied.Based on the combination of carbonized sponge and RTVS,the strain sensing mechanism and piezoresistive properties are discussed.After research and testing,the CS/RTVS flexible strain sensor has excellent fast response speed and stability,and the maximum strain coefficient of the sensor is 136.27.In this study,the self-developed CS/RTVS sensor was used to monitor the movements of the wrist joint,arm elbow joint and fingers in real time.Research experiments show that CS/RTVS flexible strain sensor has good application prospects in the field of human motion monitoring.

A Test Method for the Static/Moving State of Targets Applied to Airport Surface Surveillance MLAT System

Due to the particularity of its location algorithm,there are some unique difficulties and features regarding the test of target motion states of multilateration(MLAT)system for airport surface surveillance.This paper proposed a test method applicable for the airport surface surveillance MLAT system,which can effectively determine whether the target is static or moving at a certain speed.Via a normalized test statistic designed in the sliding data window,the proposed method not only eliminates the impact of geometry Dilution of precision(GDOP)effectively,but also transforms the test of different motion states into the test of different probability density functions.Meanwhile,by adjusting the size of the sliding window,it can fulfill different test performance requirements.The method was developed through strict theoretical extrapolation and performance analysis,and simulations results verified its correctness and effectiveness.

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.

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.

Fast-response,high-sensitivity multi-modal tactile sensors based on PPy/Ti_(3)C_(2)T_(x) films for multifunctional applications

In recent years,multi-modal flexible tactile sensors have become an important direction in the development of electronic skin because of their excellent sensitivity,flexibility and wearable properties.In this work,a humidity-pressure multi-modal flexible sensor based on polypyrrole(PPy)/Ti_(3)C_(2)T_(x) sensitive film packaged with porous polydimethylsiloxane(PDMS)is investigated by combining the sensitive structure generation mechanism of in situ polymerization to achieve the simultaneous detection of humidity and pressure,which has a sensitivity of 89,113.4Ω/%RH in a large humidity range of 0%-97%RH,and response/recovery time of 2.5/1.9 s.The tactile pressure sensing has a high sensitivity,a fast response of 67/52 ms,and a wide detection limit.The device also has excellent performance in terms of stability and repeatability,making it promising for respiratory pattern and motion detection.This work provides a new solution to address the construction of multi-modal tactile sensors with potential applications in the fields of medical health,epidemic prevention.

Optical Microfber Neuron for Finger Motion Perception

To achieve dexterous motion controlling of robot,the sensors that function like human neurons for motion perception are essential.In this work,a silica microfber probe-based optical neuron(MPON)for robot fnger motion detection is proposed.The silica microfber probe was fabricated by snapping a biconical silica optical microfber that drawn from the standard optical fbre.Then it was embedded into thin polydimethylsiloxane(PDMS)to detect and recognize motions of robotic fnger.Specifcally,a PDMS-Tefon-Microfber-Tefon-PDMS composite structure was prepared to protect the waveguide structure of silica microfber probe and avoid the environmental pollution.With the help of this composite structure,the proposed MPON achieved the accurate measurement of bending angle with large range and fast response.The repeatability and stability of MPON were also investigated.Additionally,diferent fnger motions were successfully distinguished through observing the output power variation of MPON.The proposed MPON could serve as the perceptron of robot hand,which could be applied in dexterous gesture control even human machine interaction.

1D-2D nanohybrid-based textile strain sensor to boost multiscale deformative motion sensing performance

The development of strain sensors with both superior sensitivity(gauge factor(GF)>100)and broad strain-sensing range(>50%strain)is still a grand challenge.Materials,which demonstrate significant structural deformation under microscale motion,are required to offer high sensitivity.Structural connection of materials upon large-scale motion is demanded to widen strainsensing range.However,it is hard to achieve both features simultaneously.Herein,we design a crepe roll structure-inspired textile yarn-based strain sensor with one-dimensional(1D)-two-dimensional(2D)nanohybrid strain-sensing sheath,which possesses superior stretchability.This ultrastretchable strain sensor exhibits a wide and stable strain-sensing range from microscale to large-scale(0.01%–125%),and superior sensitivity(GF of 139.6 and 198.8 at 0.01%and 125%,respectively)simultaneously.The strain sensor is structurally constructed by a superelastic 1D-structured core elastomer polyurethane yarn(PUY),a novel high conductive crepe roll-structured(CRS)1D-2D nanohybrid multilayer sheath which assembled by 1D nanomaterials silver nanowires(AgNWs)working as bridges to connect adjacent layers and 2D nanomaterials graphene nanoplates(GNPs)offering brittle lamellar structure,and a thin polydopamine(PDA)wrapping layer providing protection in exterior environment.During the stretching/deformation process,microcracks originate and propagate in the GNPs lamellar structure enable resistance to change significantly,while AgNWs bridge adjacent GNPs to accommodate applied stress partially and boost strain.The 1D crepe roll structure-inspired strain sensor demonstrates multifunctionality in multiscale deformative motion detection,such as respiratory motions of Sprague–Dawleyw rat,flexible digital display,and proprioception of multi-joint finger bending and antagonistic flexion/extension motions of its flexible continuum body.

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.

Recent Progress on Smart Fiber and Textile Based Wearable Strain Sensors:Materials,Fabrications and Applications

With the rapid development of smart products,fexible and stretchable smart wearable electronic devices gradually play an important role,and they are considered as the pioneers of the new generation of fexible electronic devices.Among these intelligent devices,fexible and stretchable strain sensors have been widely studied for their good fexibility,high sensitivity,high repeatability and huge potential for application in personal healthcare and motion detection.Moreover,unlike traditional rigid bulky sensors,the high-performance fexible strain sensors are lightweight portable devices with excellent mechanical and electrical performance,which can meet personalized needs and become more popular.Herein,the research progress of fexible strain sensors in recent years are reviewed,which mainly introducing the sensing principles and key parameters of strain sensors,commonly used conductive materials and fexible substrates and common preparation methods,and fnally proposes the future application and prospects of strain sensors.

Wearable and washable textile-based strain sensors via a single-step, environment-friendly method

Recently, soft and stretchable strain sensors that can be incorporated into textiles have attracted significantly increasing interest for use in a diverse range of applications. However, the simple fabrication of stretchable devices that exhibit excellent sensing performance, are highly durability and are a good fit to the human body remains a challenge. Herein, we describe the fabrication of a new flexible strain sensor on a traditional polyester fabric using a one-step method that involves the reduction of graphene oxide(GO) using ascorbic acid(L-AA). The resulting textile-based strain sensors could be washed, exhibited long-term stability,and had a negative linear response that gave a good sensing response when used in wearable applications. In addition to effectively detecting human motions, the textile was modified such that it could detect ultra-large deformations. The impressive mechanical performance, durability and the ability to capture and monitor a variety of human actions and motions mean that these textile-based sensors have great potential in biomonitoring, soft co-robotics, and human-machine interactions.

Camera-based Basketball Scoring Detection Using Convolutional Neural Network

Recently,deep learning methods have been applied in many real scenarios with the development of convolutional neural networks(CNNs).In this paper,we introduce a camera-based basketball scoring detection(BSD)method with CNN based object detection and frame difference-based motion detection.In the proposed BSD method,the videos of the basketball court are taken as inputs.Afterwards,the real-time object detection,i.e.,you only look once(YOLO)model,is implemented to locate the position of the basketball hoop.Then,the motion detection based on frame difference is utilized to detect whether there is any object motion in the area of the hoop to determine the basketball scoring condition.The proposed BSD method runs in real-time with satisfactory basketball scoring detection accuracy.Our experiments on the collected real scenario basketball court videos show the accuracy of the proposed BSD method.Furthermore,several intelligent basketball analysis systems based on the proposed method have been installed at multiple basketball courts in Beijing,and they provide good performance.

Regenerating leather waste for flexible pressure sensing applications

Pressure sensor can be applied in a wide range of fields,such as voice recognition,human motions detection and artificial electronic skin,the sensing of which is greatly influenced by the flexibility and stretchability of substrate materials.Here,based on the piezoresistive effect,new kinds of flexible pressure sensors have been realized from a pair of flexible and biocompatible collagen films:one is coated by silver nanowires(Ag NWs)and the other by interdigital electrode,respectively.The collagen films are regenerated from leather waste and could bring economic benefits to society.The prepared pressure sensors are applied for voice recognition and human motion detection.