Large Amount Of Video Data Research Articles

A novel automated rodent tracker (ART), demonstrated in a mouse model of amyotrophic lateral sclerosis

BackgroundGenerating quantitative metrics of rodent locomotion and general behaviours from video footage is important in behavioural neuroscience studies. However, there is not yet a free software system that can process large amounts of video data with minimal user interventions. New methodHere we propose a new, automated rodent tracker (ART) that uses a simple rule-based system to quickly and robustly track rodent nose and body points, with minimal user input. Tracked points can then be used to identify behaviours, approximate body size and provide locomotion metrics, such as speed and distance. ResultsART was demonstrated here on video recordings of a SOD1 mouse model, of amyotrophic lateral sclerosis, aged 30, 60, 90 and 120days. Results showed a robust decline in locomotion speeds, as well as a reduction in object exploration and forward movement, with an increase in the time spent still. Body size approximations (centroid width), showed a significant decrease from P30. Comparison with existing method(s)ART performed to a very similar accuracy as manual tracking and Ethovision (a commercially available alternative), with average differences in coordinate points of 0.6 and 0.8mm, respectively. However, it required much less user intervention than Ethovision (6 as opposed to 30 mouse clicks) and worked robustly over more videos. ConclusionsART provides an open-source option for behavioural analysis of rodents, performing to the same standards as commercially available software. It can be considered a validated, and accessible, alternative for researchers for whom non-invasive quantification of natural rodent behaviour is desirable.

Energy efficient processing of motion estimation for embedded multimedia systems

Visual sensor networks require low power compression techniques of large amount of video data in each camera node due to the energy-constrained and bandwidth-limited environments. In this paper, energy-efficient architecture for Variable Block Size Motion Estimation is proposed to fully utilize dynamic partial reconfiguration capability of programmable hardware fabric in distributed embedded vision processing nodes. Partial reconfiguration of FPGA is exploited to support run-time reconfiguration of the proposed modular hardware architecture for motion estimation. According to the required search range, hardware reconfiguration is performed adaptively to reduce the hardware resources and power consumption. A reconfigurable ME ranging from simple 1-D to a complex 2-D Sum of Absolute Differences (SAD) array to perform full search block matching is selected in order to support different search window size. The implemented scalable SAD array can provide different resolutions and frame rates for real time applications with multiple reconfigurable regions.

A data mining approach in real-time measurement for polymer additive manufacturing process with exposure controlled projection lithography

Real-time inspection and part dimensions determination during the manufacturing process can improve production of qualified parts. Exposure Controlled Projection Lithography (ECPL) is a bottom-up mask-projection additive manufacturing (AM) process, in which micro parts are fabricated from photopolymers on a stationary transparent substrate. An in-situ interferometric curing monitoring and measuring (ICM&M) system has been developed to infer the output of cured height. Successful ICM&M practice of data acquisition and analysis for retrieving useful information is central to the success of real-time measurement and control for the ECPL process. As the photopolymerization phenomena occur continuously over a range of space and time scales, the ICM&M data analysis is complicated with computation speed and cost. The large amount of video data, which is usually noisy and cumbersome, requires efficient data analysis methods to unleash the ICM&M capability. In this paper, we designed a pragmatic approach of ICM&M data mining to intelligently decipher part height across the cured part. As a data-driven measurement method, the ICM&M algorithms are strengthened by incorporating empirical values obtained from experimental observations to guarantee realistic solutions, and they are particularly useful in real time when limited resource is accessible for online computation. Experimental results indicate that the data-enabled ICM&M method could estimate the height profile of cured parts with accuracy and precision. The study exemplifies that data mining techniques can help realize the desired real time measurement for AM processes, and help unveil more insights about the process dynamics for advanced modeling and control.

Migration and interaction tracking for quantitative analysis of phagocyte–pathogen confrontation assays

Invasive fungal infections are emerging as a significant health risk for humans. The innate immune system is the first line of defense against invading micro-organisms and involves the recruitment of phagocytes, which engulf and kill pathogens, to the site of infection. To gain a quantitative understanding of the interplay between phagocytes and fungal pathogens, live-cell imaging is a modern approach to monitor the dynamic process of phagocytosis in time and space. However, this requires the processing of large amounts of video data that is tedious to be performed manually. Here, we present a novel framework, called AMIT (algorithm for migration and interaction tracking), that enables automated high-throughput analysis of multi-channel time-lapse microscopy videos of phagocyte-pathogen confrontation assays. The framework is based on our previously developed segmentation and tracking framework for non-rigid cells in brightfield microscopy (Brandes etal., 2015). We here present an advancement of this framework to segment and track different cell types in different video channels as well as to track the interactions between different cell types. For the confrontation assays of polymorphonuclear neutrophils (PMNs) and Candida glabrata considered in this work, the main focus lies on the correct detection of phagocytic events. To achieve this, we introduced different PMN states and a state-transition model that represents the basic principles of phagocyte-pathogen interactions. The framework is validated by a direct comparison of the automatically detected phagocytic activity of PMNs to a manual analysis and by a qualitative comparison with previously published analyses (Duggan etal., 2105; Essig etal., 2015). We demonstrate the potential of our algorithm by comprehensive quantitative and multivariate analyses of confrontation assays involving human PMNs and the fungus C. glabrata.

Scalable video summarization via sparse dictionary learning and selection simultaneously

Every day, a huge amount of video data is generated worldwide and processing this kind of data requires powerful resources in terms of time, manpower, and hardware. Therefore, to help quickly understand the content of video data, video summarization methods have been proposed. Recently, sparse formulation-based methods have been found to be able to summarize a large amount of video compared to other methods. In this paper, we propose a new method in which video summarization is performed as training and selection sparse dictionary problem simultaneously. It is shown that the proposed method is able to improve the summarization of a large amount of video data compared to other methods. Finally, the performance of the proposed method is compared to state-of-the-art methods using standard data sets, in which the key frames are manually tagged. The obtained results demonstrate that the proposed method improves video summarization compared to other methods.

Analysis of microcirculation measurements by novice users trained by a standardized interactive tutorial: An inter-observer variability study.

Excessive time for analysis may impede microcirculatory studies with large amounts of video data. Engaging more personnel in the analyses seems to be a rational approach in that scenario and could shorten the time-interval between capturing images and obtaining results. Our hypothesis was that novice users would be able to determine standard microcirculatory parameters using a semi-automated software with an acceptable degree of variability after participating in a standardized interactive training session. 14 volunteers were included in the study. All volunteers analyzed separately the same sample video after the training. The kappa statistic was calculated for the primary outcome parameter microvascular flow index (MFI) within small and large vessels and indicated a fair level of agreement in the results of the novice users. A standardized interactive tutorial can be useful to teach microcirculatory analysis in previously untrained subjects.

Summarization of Egocentric Videos: A Comprehensive Survey

The introduction of wearable video cameras (e.g., GoPro) in the consumer market has promoted video life-logging, motivating users to generate large amounts of video data. This increasing flow of first-person video has led to a growing need for automatic video summarization adapted to the characteristics and applications of egocentric video. With this paper, we provide the first comprehensive survey of the techniques used specifically to summarize egocentric videos. We present a framework for first-person view summarization and compare the segmentation methods and selection algorithms used by the related work in the literature. Next, we describe the existing egocentric video datasets suitable for summarization and, then, the various evaluation methods. Finally, we analyze the challenges and opportunities in the field and propose new lines of research.

Reconstruction-Free Action Inference from Compressive Imagers.

Persistent surveillance from camera networks, such as at parking lots, UAVs, etc., often results in large amounts of video data, resulting in significant challenges for inference in terms of storage, communication and computation. Compressive cameras have emerged as a potential solution to deal with the data deluge issues in such applications. However, inference tasks such as action recognition require high quality features which implies reconstructing the original video data. Much work in compressive sensing (CS) theory is geared towards solving the reconstruction problem, where state-of-the-art methods are computationally intensive and provide low-quality results at high compression rates. Thus, reconstruction-free methods for inference are much desired. In this paper, we propose reconstruction-free methods for action recognition from compressive cameras at high compression ratios of 100 and above. Recognizing actions directly from CS measurements requires features which are mostly nonlinear and thus not easily applicable. This leads us to search for such properties that are preserved in compressive measurements. To this end, we propose the use of spatio-temporal smashed filters, which are compressive domain versions of pixel-domain matched filters. We conduct experiments on publicly available databases and show that one can obtain recognition rates that are comparable to the oracle method in uncompressed setup, even for high compression ratios.

A Distributed Video Management Cloud Platform Using Hadoop

Due to complexities of big video data management, such as massive processing of large amount of video data to do a video summary, it is challenging to effectively and efficiently store and process these video data in a user friendly way. Based on the parallel processing and flexible storage capabilities of cloud computing, in this paper, we propose a practical massive video management platform using Hadoop, which can achieve a fast video processing (such as video summary, encoding, and decoding) using MapReduce, with good usability, performance, and availability. Red5 streaming media server is used to get video stream from Hadoop distributed file system, and Flex is used to play video in browsers. A user-friendly interface is designed for managing the whole platform in a browser–server style using J2EE. In addition, we show our experiences on how to fine-tune the Hadoop to get optimized performance for different video processing tasks. The evaluations show that the proposed platform can satisfy the requirements of massive video data management.

REVIEW ON CONTENT BASED VIDEO LECTURE RETRIEVAL

Recent advances in multimedia technologies allow the capture and storage of video data with relatively inexpensive computers. Furthermore, the new possibilities offered by the information highways have made a large amount of video data publicly available. However, without appropriate search techniques all these data are hardly usable. Users are not satisfied with the video retrieval systems that provide analogue VCR functionality. For example, a user analyses a soccer video will ask for specific events such as goals. Content-based search and retrieval of video data becomes a challenging and important problem. Therefore, the need for tools that can be manipulate the video content in the same way as traditional databases manage numeric and textual data is significant. Therefore, a more efficient method for video retrieval in WWW or within large lecture video archives is urgently needed. This project presents an approach for automated video indexing and video search in large lecture video archives. First of all, we apply automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. Subsequently, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames and Automatic Speech Recognition on lecture audio tracks.

Predicting the distribution of deep-sea vulnerable marine ecosystems using high-resolution data: Considerations and novel approaches

Little is known about species distribution patterns in deep-sea environments, primarily because sampling surveys in the high seas are expensive and time consuming. The increasing need to manage and protect vulnerable marine ecosystems, such as cold-water corals, has motivated the use of predictive modelling tools, which produce continuous maps of potential species or habitat distribution from limited point observations and full coverage environmental data. Rapid advances in acoustic remote sensing, oceanographic modelling and sampling technology now provide high quality datasets, facilitating model development with high spatial detail. This paper provides a short overview of existing methodologies for predicting deep-sea benthic species distribution, and illustrates emerging issues related to spatial and thematic data resolution, and the use of transect-derived species distribution data. In order to enhance the ecological relevance and reliability of deep-sea species distribution models, novel techniques are presented based on a case study predicting the distribution of the cold-water coral Lophelia pertusa in three carbonate mound provinces in Irish waters. Specifically, the study evaluates (1) the capacity of newly developed high-resolution (250m grid cell size) hydrodynamic variables to explain local scale cold-water coral distribution patterns, (2) the potential value of species occurrence proportion data to maintain semi-quantitative information of coral prevalence (i.e. coverage) and sampling effort per grid cell within the response variable, and (3) mixed effect modelling to deal with spatially grouped transect data. The study shows that predictive models using vertical and horizontal flow parameters perform significantly better than models based on terrain parameters only. Semi-quantitative proportion data may decrease model uncertainty and increase model reliability, and provide a fruitful avenue of research for analysing large quantities of video data in a detailed yet time-efficient manner. The study concludes with an outlook of how species distribution models could improve our understanding of vulnerable marine ecosystem functioning and processes in the deep sea.

A context-aware mobile web middleware for service of surveillance video with privacy

Video surveillance systems are becoming ubiquitous nowadays. To protect citizens' privacy and to be compliant with legal requirements, surveillance video cannot generally be accessed in public places. However, a large amount of video data is often still required to provide timely services in these places. This work proposes a novel approach to support a developer to improve the service access to surveillance video. Firstly, a mobile web middleware architecture for the development of context-awareness applications, using NFC (Near Field Communication) and a new framework for NFC utilization by analyzing and constructing a service model, are proposed. Based on them, the efficient support for acquiring, interpreting and accessing contexts is provided, and more advanced applications with context-awareness and adaptability to their changing contexts are allowed. Moreover, a privacy-aware approach for video communication in our middleware is proposed. Symmetric key encryption and public-key encryption techniques are used to ensure privacy information security. A real-time scrambling approach to conceal video information is presented. Finally, we present the design and the implementation of the context-aware middleware and validate the expressiveness of application logics through prototype development.

Automated and coupled services of advanced smart surveillance systems toward green IT: tracking, retrieval and digital evidence

Green Security is a new research field defining and investigating security solutions using an energy-aware perspective. Growing efforts and interests for an intelligent or smart surveillance system which is capable of automatically detecting and tracking target objects is in the spotlight in the security community. So far, these technologies are mainly aimed at single camera applications and are evolving with the demand for wide-area surveillance systems currently. However, the tracking techniques used on a single camera have limitations in providing effective crime prevention and countermeasures when an incident occurs since an object is not linked to other cameras. In addition, the use of multi-camera systems for wide-area surveillance not only produces large amounts of video data to be stored, but also have more technical requirements in the interrelation between cameras or server. It require a considerable amount of time, manpower and energy in multi-camera tracking and back-tracking of objects. Therefore, we propose the advanced smart surveillance system for wide-areas which is capable of the automated tracking and retrieval of target object and digital evidence-video collection. Furthermore, we considered the multiple-camera environment with non-overlapping views which includes more constraint conditions by various light changes. This system enables real-time object tracking, fast post-retrieval and selective digital evidence collection with economy of time, manpower, memory devices, and energy consumption. Also, this system is more energy-efficient since our schemes are organically connected to each other.

Efficient RGB to YCbCr Color Space Conversion for Embedded Application

Modern embedded portable devices usually have to deal with large amounts of video data. Due to massive floating-point multiplications, the color space conversion is inefficient on the embedded processor. Considering the characteristics of RGB to YCbCr color space conversion, this paper proposed a strategy for truncated-based LUT Multiplier (T-LUT Multiplier). On this base, an original approach converting RGB to YCbCr is presented which employs the T-LUT Multiplier and the pipeline-based adder. Experimental results demonstrate that the proposed method can obtain maximum operating frequency of 358MHz, 3.5 times faster than the direct method. Furthermore, the power consumption is less than that of the general method approximately by 15%~27%.

Adaptive Threshold Video Shot Boundary Detection Algorithm Based on Progressive Bisection Strategy

To resolve the problems of large amount of video data and gradual transition detection, it proposes an originally progressive bisection strategy to control the system process. To address the problem of inaccurate detection threshold, it develops the adaptive threshold scheme based on inter-frame difference histogram. For the problem of using multiple features to detect shot boundaries, it put forwards a new Boolean weighting algorithm to effectively determine the final results. It performs extensive experiments on TRECVID video set, compares the results with other algorithms and the proposed algorithm is superior both in detection quality and time complexity.

Research on Information Applied Technology with Video Compression Algorithms Based on the Optimal Multi-Band Haar Wavelet Transform

Video playback has been one of the most important online communication ways. With the application of stereo video, large amount of video data need to be stored and transported so that fluency and clarity of demand system, and how to efficiently conduct compressed encoding for stereoscopic video data becomes a hot topic currently. In view of this problem, this paper puts forward the video-on-demand compression algorithm based on the optimal multi-band Haar wavelet transform, through the research on wavelet transform algorithm model to reinforce the algorithm secondly, strengthening from the binary wavelet theory into octal wavelet system theory to get better compression capability. The simulation experiments show that video-on-demand compression algorithm based on the optimal multi-band Haar wavelet transform proposed in this paper has a good compression performance not only under medium and high bit- rate conditions, and also reaches the H. 263 under low bit-rate condition.

Hierarchical Architecture for HEVC Parallel Encoder

The Emerging High Efficiency Video Coding (HEVC) standard offers a significantly better compression rate and higher video quality. HEVC encoders create very high CPU demand, and it is hard for a single core computer to deal with such complex coding computations. To process the high workload of HEVC coding for large-scale video data, the current HEVC draft contains several parallelizing approaches: tile-based parallelization and WPP-level parallelization. In this paper, we adopt additional data parallelism, GOP partitioning, to implement a parallel encoder. We propose the scalable cluster architecture of the HEVC encoder to achieve scalability and a high encoding speed by combining two levels of parallelism, GOP-level parallelism and the HEVC parallel method. The proposed scheme can reduce the large encoding time and significantly improve the coding efficiency. The proposed scalable cluster system is very suitable for high-resolution video such as 4K or 8K containing large amounts of video data.

Link Mining: A Computer Vision and Pattern Mining Approach

This work addresses the important problem of discovery and analysis of social networks and link between frequent people observed from surveillance video footage where large amount of video data is collected routinely. A computer vision approach has been applied for detecting and extracting people within a group using HAAR classifier. This technique allows us to recognizing people by doing similarity matching between training faces and unknown detected face image. Therefore it is required to obtain high resolution face images of people in order to extract intrinsic feature information of facial images used in detection of person’s faces. We present a novel frequent pattern mining based approach in the domain of frequent person detection i.e. apriori to solve frequent association problem between social networks obtained from low level task of face recognition. Our approach is illustrated with promising results from a fully integrated camera system. General Terms Data Mining, Frequent Pattern Mining, Social Network Analysis, Dynamic Social Network Analysis

System of a programmable-chip-based image-processing system

Recent advances in semiconductor technology have made it possible to integrate an entire system including processors, memory, and other system units into a single programmable chip [a field programmable gate array (FPGA)]. These configurations, called system on a programmable chip (SOPC), have the advantage of being designed and implemented quicker than traditional technologies and are relatively cheap to produce for low volume applications. One processor-intensive application, which is ideal for SOPC technology, is that of image processing where there is a repeated application of operations on the two-dimensional data. We present the results of research that investigate the use of SOPC technology in building a real-time image-processing system with the capability of performing video acquisition, image processing, and image display. In order to solve the complex on-chip data communication, while not degrading the transfer speed of large amounts of video data, a novel and effective bus arbitration solution called “simultaneously multimastering Avalon streaming transfer with peripheral-controlled waitrequest” is developed. Rather than using the software approach to initialize direct memory access (DMA) like transfers, this solution takes advantage of the FPGA hardware resources to perform bus arbitration and hence increase system efficiency.

Video Query Using Temporal Signature and Similarity Matching

Abstract. Large amount of video data is stored and distributed in wide variety of application. Due to the fast video material increases, manage and query of video become more and more important. In this paper, we address a temporal signature representation and similarity model to retrieval the similar video within database by video query. Experimental results on real date are presented. The experimental results show that the statistical approach permits accurate query of video clip, in particular, the performance of the approach was found extremely satisfactory with determine all similar video in database.