Video Data Streams Research Articles

YOLO based Human Action Recognition and Localization

Human action recognition in video analytics has been widely studied in recent years. Yet, most of these methods assign a single action label to video after either analyzing a complete video or using classifier for each frame. But when compared to human vision strategy, it can be deduced that we (human) require just an instance of visual data for recognition of scene. It turns out that small group of frames or even single frame from the video are enough for precise recognition. In this paper, we present an approach to detect, localize and recognize actions of interest in almost real-time from frames obtained by a continuous stream of video data that can be captured from a surveillance camera. The model takes input frames after a specified period and is able to give action label based on a single frame. Combining results over specific time we predicted the action label for the stream of video. We demonstrate that YOLO is effective method and comparatively fast for recognition and localization in Liris Human Activities dataset.

An intelligent traffic engineering method for video surveillance systems over software defined networks using ant colony optimisation

Nowadays, software defined network (SDN) is an innovative technology for provisioning quality of service (QoS) requirements. SDN network management facilitated using software in which network administrator can perform desired traffic engineering techniques on different applications. Video streaming in video surveillance systems is a critical application which needs QoS requirements such as low packet loss and short delay. These requirements can be satisfied by using traffic engineering techniques over SDNs. In this paper, an intelligent traffic engineering technique for a video surveillance system over SDN is proposed. It is based on constrained shortest path (CSP) problem in which the packet loss and delay of video streaming data should be significantly reduced. Due to NP-completeness of the CSP problems, in this paper, ant colony optimisation algorithm is used to solve it. To the best of our knowledge, this is the first traffic engineering technique used ant colony for video streaming over SDN. Comparisons between the proposed method and prevalent methods such as OSPF routing protocol and LARAC optimisation algorithm demonstrated the effectiveness of the proposed method in terms of packet loss, delay and peak signal-to-noise ratio (PSNR). It was shown that using the proposed method will also ameliorate the traffic engineering for video surveillance systems.

Modified Hermite Pulse-Based Wideband Communication for High-Speed Data Transfer in Wireless Sensor Applications

With technological advances in the field of communication, the need for reliable high-speed data transfer is increasing. The deployment of large number of wireless sensors for remote monitoring and control and streaming of high definition video, voice and image data, etc. are imposing a challenge to the existing network bandwidth allocation for reliable communication. Two novel schemes for ultra-wide band (UWB) communication technology have been proposed in this paper with the key objective of intensifying the data rate by taking advantage of the orthogonal properties of the modified Hermite pulse (MHP). In the first scheme, a composite pulse is transmitted and in the second scheme, a sequence of multi-order orthogonal pulses is transmitted in the place of a single UWB pulse. The MHP pulses exhibit a mutually orthogonal property between different ordered pulses and due to this property, simultaneous transmission is achieved without collision in the UWB system, resulting in an increase in transmission capacity or improved bit error rate. The proposed schemes for enhanced data rate will offer high volume data monitoring, assessment, and control of wireless devices without overburdening the network bandwidth and pave the way for new platforms for future high-speed wireless sensor applications.

Progressive Streaming of Video Data for Traffic Surveillance

Traffic surveillance has been one of the essential attributes in smart city concept. Nowadays, in such applications rotating camera is preferred over static camera. Motivation behind this substitution is to reduce the cost of data transmission and Total of cost of ownership. To design an optimal and performant wireless ‘smart city area network’ for video surveillance systems, this paper focuses on some key areas, namely, transmission efficiency, lossless video data coding, data congestion, edge computing at transmission nodes. The end objective is to achieve high quality received video stream in spite of compressed data transmission. Some research initiatives in this domain are pertinent. For example, Structural Similarity Index (SSIM) based rate distortion optimization is an effective tool in enhancing the perceptual video quality in wireless environments. However, prevailing system does not consider the network congestion conditions, affecting quality of received video. Also, effect of distortion introduced by ‘channel noise’ is unattended. This motivated us to propose a new dual metric traffic control mechanism, wherein both metrics i.e. distortion and data congestion are considered. It is based on an ‘improvised SSIM’ method which incorporates the ‘Rate of allocation’ algorithm as a function. Experimental results unveil that the proposed traffic control using similarity index under noise diversity can achieve better video quality and more data throughput.

Application and Implementations of the Principle of Constant Color Luminance in Digital Video Coding Systems

Modern hardware systems of processing the video data stream for color coding apply the principle of constant brightness proposed in the development of the NTSC color coding system. This principle, like its implementation, is not free from drawbacks: loss of information on the clarity of the encoded color images, degradation of clarity in achromatic details and images as the color saturation increases, etc. In addition, the use of video data decoding formats in digital video data processing systems, such As 4:2:2, 4:2:0, 4:1:1, distorts the decoded video image. An alternative approach for encoding a color video stream is to apply the principle of constant color luminance. The work describes the coding according to the principle of constant color luminance. A comparative analysis of the transformed images is carried out with the help of the two principles given. The advantage of applying the principle of constant color brightness in digital video coding systems is shown. It is shown that using the principle of constant color brightness it is possible to obtain a gain of more than 6 dB. The implementation of the principle of constant color luminance for real and integer modern hardware platforms is described. A comparative analysis of the realizations of the principles of constant brightness and constant color luminance was carried out, showing the advantage of applying the principle of constant color luminance for some modern processors. The application of the principle of constant color luminance in digital video encoding systems can help improve the quality of recoverable color coded images.

Architecture for parallel marker-free variable length streams decoding

Due to throughput requirements above 1 gigapixel/sec for the real-time compression of modern image and video data streams, parallelism for encoding and decoding is inevitable. To achieve parallel decoding, a well-established technique is to insert markers into the variable length code (VLC) stream. By locating markers, it is then possible to extract the sub-streams that are, in turn, decoded in parallel. The use of markers adversely affects compression especially when a high degree of parallelism is required. In this paper, we propose an architecture of a marker-free parallel decoding approach of VLC streams. Instead of multiple local entropy decoders, the proposed architecture is based on using a single parallel entropy decoder in conjunction with a novel format to construct the VLC stream. The approach runs at high clock rates supporting parallelism to a high number of decoders. A synthesized clock frequency well above 110 MHz is achieved for up to 20 decoders on a medium-sized FPGA.

Content Placement for Video-on-Demand Services Over Cellular Networks

The issues of content placement and content replication for video-on-demand streaming over cellular networks are addressed in this study. Using many replications of a relatively small number of the most popular items a significant performance improvement can be achieved. Our method was verified using real video streaming data taken from traces of live content distribution networks. Simulation results show that replicating a relatively small number of video files can significantly reduce the incoming bandwidth from the Internet backbone, as well as the (time) latency for content delivery. The proposed scheme is particularly suitable for IP-based TV services, for which the content popularity can be very often predicted with relatively high accuracy. In addition, we propose a hybrid cache management scheme, in which the cache is partitioned into two components. The first component is for long-term items, and it is updated relatively rarely, while the second component is updated more frequently.

Prototype holographic data storage drive with wavefront compensation for playback of 8K video data

We developed a prototype holographic data storage drive and verified the feasibility of using it for archival purposes. Holograms recorded on a recording medium become distorted as a result of photopolymerization and changes in ambient temperature. For stable reproduction, wavefront compensation technology was incorporated in the drive. The hologram distortion was optically compensated by controlling the wavefront of the laser beam. We also applied this method to the wavelength adjustment in data reproduction and confirmed that it expanded the wavelength shift margin. We configured the drive for 8K UHDTV playback and successfully reproduced compressed 8K UHDTV video stream data recorded on a holographic disc in real time.

Continuous Prediction of Streaming Video QoE Using Dynamic Networks

Streaming video data accounts for a large portion of mobile network traffic. Given the throughput and buffer limitations that currently affect mobile streaming, compression artifacts and rebuffering events commonly occur. Being able to predict the effects of these impairments on perceived video quality of experience (QoE) could lead to improved resource allocation strategies enabling the delivery of higher quality video. Toward this goal, we propose a first of a kind continuous QoE prediction engine. Prediction is based on a nonlinear autoregressive model with exogenous outputs. Our QoE prediction model is driven by three QoE-aware inputs: An objective measure of perceptual video quality, rebuffering-aware information, and a QoE memory descriptor that accounts for recency. We evaluate our method on a recent QoE dataset containing continuous time subjective scores.

Acute Toxicity and Etho-toxicity of Three Insecticides Used for Mosquito Control on Amphibian Tadpoles

Among the measures used to manage mosquito populations and prevent human diseases, the application of pesticides is the global strategy mostly employed. To investigate the lethal and sublethal effects of insecticides used to control mosquitoes on amphibians (Rhinella arenarum, Rhinella fernandezae, and Physalaemus albonotatus), tadpoles were exposed to commercial formulations of temephos (Abate®), Bacillus thuringiensis var. israelensis (Introban®), and permethrin (Depe®). Their acute toxicity in terms of median lethal concentration (LC50) and no- (NOEC) and lowest-observed-effect concentrations (LOEC) was evaluated. To assess the sublethal effects on behavioral endpoints, tadpoles were exposed to the NOEC-24-h value of each insecticide. After that, tadpoles were recorded and video-streaming data were processed by Smart® software. Based on LC50, permethrin was the most toxic insecticide, followed by temephos and B. thuringiensis var. israelensis. Also, intraspecific and interspecific susceptibilities of tadpoles to insecticides were observed. Regarding behavior, the exposure of R. arenarum to the three insecticides had a significant effect on all behavioral endpoints. Two of the three swimming parameters evaluated for R. fernandezae were affected by permethrin, and in the end, only one behavioral pattern was altered in P. albonotatus after exposure to temephos. These results showed that tadpoles’ species were affected differently depending on the chemical properties of the pesticide and on a dose-response effect of the insecticides. Overall, our study suggests that further research is needed to quantify the potential damage of pyrethroid insecticides used for mosquito control on non-target aquatic organisms, mainly due to etho-toxic effects.

Prediction of Manipulation Actions

By looking at a person’s hands, one can often tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor’s intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded videos of subjects performing different manipulation actions on the same object, such as “squeezing”, “flipping”, “washing”, “wiping” and “scratching” with a sponge. In psychophysical experiments, we evaluated human observers’ skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object. We then developed a recurrent neural network based method for action prediction using as input image patches around the hand. We also used the same formalism to predict the forces on the finger tips using for training synchronized video and force data streams. Evaluations on two new datasets show that our system closely matches human performance in the recognition task, and demonstrate the ability of our algorithms to predict in real time what and how a dexterous action is performed.

Handover Based IMS Registration Scheme for Next Generation Mobile Networks

Next generation mobile networks aim to provide faster speed and more capacity along with energy efficiency to support video streaming and massive data sharing in social and communication networks. In these networks, user equipment has to register with IP Multimedia Subsystem (IMS) which promises quality of service to the mobile users that frequently move across different access networks. After each handover caused due to mobility, IMS provides IPSec Security Association establishment and authentication phases. The main issue is that unnecessary reregistration after every handover results in latency and communication overhead. To tackle these issues, this paper presents a lightweight Fast IMS Mobility (FIM) registration scheme that avoids unnecessary conventional registration phases such as security associations, authentication, and authorization. FIM maintains a flag to avoid deregistration and sends a subsequent message to provide necessary parameters to IMS servers after mobility. It also handles the change of IP address for user equipment and transferring the security associations from old to new servers. We have validated the performance of FIM by developing a testbed consisting of IMS servers and user equipment. The experimental results demonstrate the performance supremacy of FIM. It reduces media disruption time, number of messages, and packet loss up to 67%, 100%, and 61%, respectively, as compared to preliminaries.

Delay Insertion Based P2PTV Traffic Localization Considering Peer's Relaying Capability

Recently, P2PTV is a popular application to deliver video streaming data over Internet network. On the overlay network, P2PTV applications create the logical links between pairs of peers considering round trip time (RTT) without physical network consideration. P2PTV packets are shared over a network without localization awareness which is the serious problem for Internet Service Providers (ISPs). The delay-insertion-based traffic localization scheme was proposed for solving this problem. However, this scheme sometimes leads the newly joining peer to download streaming traffic from a local neighbor peer which has only scarce upload bandwidth. This paper proposes a novel scheme of delay-insertion-based traffic localization in which the router estimates relay capability to each relay peer candidate and leads the newly joining peer to connect to a neighbor peer with sufficient performance for relaying video data. Parameters were evaluated for the optimized condition in the relay capability estimation process. In addition, experiments conducted on a real network show that our proposed scheme can lead the newly joining peer to avoid downloading video data from insufficient relay capability peers and maintain video quality close to normal P2PTV system while efficient traffic localization in level of Autonomous System (AS) network.

An Application of Convolutional Neural Networks to Aircraft Ejection Seat Testing

The testing of aircraft ejection seats has been an important aspect of testing in the aerospace industry for several decades. Identification and tracking of aircraft ejection seat systems, during field testing, has practical benefits in post-test analysis and real-time tracking applications. This paper proposes a methodology to identify and locate ejection seat systems in a video stream as they are propelled from a dynamic aircraft test bed. The field testing environment is cluttered with natural and man-made artifacts that effect the background appearance and make the video stream data visually noisy. The identification problem involves the extraction of critical information from the video stream and feeding it into a deep learning convolutional neural network that determines the location of the ejection system within the field of view. The results achieved, using the convolutional neural network to identify features of the ejection seat system, were promising. In particular, the convolutional neural network demonstrated an ability to identify objects that were in a different spatial orientation (rotation) than were used to train the network.

SECURE VIDEO CODED SYSTEM MODEL

In this paper, overall system model, shown in Figure (1), of video compression-encryption-transmitter/decompression-dencryption-receiver was designed and implemented. The modified video codec system has used and in addition to compression/decompression, theencryption/decryption video signal by using chaotic neural network (CNN) algorithm was done. Both of quantized vector data and motion vector data have been encrypted by CNN. The compressed and encrypted video data stream has been sent to receiver by using orthogonal frequency division multiplexing (OFDM) modulation technique. The system model was designed according to video signal sample size of 176 × 144 (QCIFstandard format) with rate of 30 frames per second. Overall system model integrates and operates successfully with acceptable performance results.

Priority-Aware Packet Pre-marking for DiffServ Architecture Based on H.264/SVC Video Stream Structure

The H264/SVC codec allows for generation of hierarchical video streams. In the stream of this type video data belonging to different layers have different priority depending on their importance to the quality of the video and the decoding process. This creates new demands on the mechanisms of packet marking, and thus new challenges for the policy guaranteeing QoS parameters, such as those defined in the DiffServ architecture. Therefore, mechanisms of the traffic engineering used in the DiffServ network should, as far as possible, take into account internal distribution of priorities inside video streams. This may be achieved by implementing an appropriate method for packet pre-marking. The paper describes the Weighted Priority Pre-marking (WPP) algorithm for priority-aware SVC video streaming over a DiffServ network. Our solution takes into account the relative importance of the Network ion Layer Units. It also does not require any changes in the implementation of the DiffServ marker algorithm. The results presented confirm that video transmission in the DiffServ domain, based on the WPP packet pre-marking, can provide better perceived video quality than the standard (best effort) streaming of multi-layered SVC video. In addition, a comparison with the transmission of the same video content encoded with the H264/AVC codec also points to the superiority of our proposed method.

An Intelligent Traffic Engineering Method over Software Defined Networks for Video Surveillance Systems Based on Artificial Bee Colony

In applications such as video surveillance systems, cameras transmit video data streams through network in which quality of received video should be assured. Traditional IP based networks cannot guarantee the required Quality of Service (QoS) for such applications. Nowadays, Software Defined Network (SDN) is a popular technology, which assists network management using computer programs. In this paper, a new SDN-based video surveillance system infrastructure is proposed to apply desire traffic engineering for practical video surveillance applications. To keep the quality of received videos adaptively, usually Constraint Shortest Path (CSP) problem is used which is a NP-complete problem. Hence, heuristic algorithms are suitable candidate for solving such problem. This paper models streaming video data on a surveillance system as a CSP problem, and proposes an artificial bee colony (ABC) algorithm to find optimal solution to manage the network adaptively and guarantee the required QoS. The simulation results show the effectiveness of the proposed method in terms of QoS metrics.

Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO).

The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle’s speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles.

New Ieee Standard For Advanced Audio Coding In Lossless Audio Compression : A Literature Review

Due to non-stop increase of communication subscribers, the loss and distortion of data (voice, video...) is emphasized. Meanwhile, IEEE-SA (Standard Association) has developed a new standard for advanced audio coding (AAC) in August 2013, called IEEE 1857.2 for contributing to those issues. This new standard for AAC is an efficient lossless audio codec (coding decoding) technique, in improving audio quality for compression and decompression, optimizing bandwidth during transmission, saving storage space, speeding up the video streaming, audio streaming and others video or audio data. This survey consists not only of describing the different existing techniques for lossless audio compression, and also of showing the state of the art of this recent lossless standard for AAC upon its most popular predecessors.

A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera.

The ubiquitous use and advancement in built-in smartphone sensors and the development in big data processing have been beneficial in several fields including healthcare. Among the basic vitals monitoring, pulse rate monitoring is the most important healthcare necessity. A multimedia video stream data acquired by built-in smartphone camera can be used to estimate it. In this paper, an algorithm that uses only smartphone camera as a sensor to estimate pulse rate using PhotoPlethysmograph (PPG) signals is proposed. The results obtained by the proposed algorithm are compared with the actual pulse rate and the maximum error found is 3 beats per minute. The standard deviation in percentage error and percentage accuracy is found to be 0.68 % whereas the average percentage error and percentage accuracy is found to be 1.98 % and 98.02 % respectively.