Search

Violence recognition is challenging because it can be presented in very different forms. For example, it can be present in an image by a person hitting another person or present in audio by a person being rude to another. Thus, audio and video are essential features to be analysed. In the audio approach, speech processing, music, and ambient sound are some of the main points of this problem since finding similarities and differences between these domains is necessary. Human activity can be classified into four different categories in the video approach, depending on the complexity and the number of body parts involved in the action. Examples of Human activity categories are considered: gestures, actions, interactions and activities. Recognizing human actions in the video becomes a challenge with this varied set of human activities. Furthermore, in the last years, the growth of deep learning techniques applied to this area has been enormous, and the reason is that their results surpass traditional signal processing on a large scale. This article is based on audio and video signals inside a vehicle to detect violence. Furthermore, the architecture used was ResNet model with Mel‐spectrogram methodology for audio signals. The proposed method for video signal representation was RGB, which applied four different models: C2D, I3D, X3D, and Flow‐Gated. Finally, multimodal fusion was applied at the end of the process. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) or 3D printing is an emerging manufacturing technology that plays a growing role in both industrial and consumer settings. However, security concerns of AM systems have been raised among researchers. In this paper, we present an online detection mechanism for the malicious attempts on AM systems, which taps into both audio and video signals collected during the printing process. For audio signals, we propose to monitor the shift of patterns in the spectrogram and dominant frequencies via a control chart designed based on the Wasserstein metric. For video signals, we propose to monitor the change in the reconstructed path of the extruder via a Hausdorff metric. We then show the effectiveness of our methods in a case study using an Ender 3D printer, where the cyber‐incidence of altering the internal fill density can be easily identified in an online manner. [ABSTRACT FROM AUTHOR]

Forensic watermarking is used to track down digital pirates after they illegally redistribute video content. Although existing algorithms often resist common signal processing attacks, they are not always robust against camcording attacks. As a solution in the state of the art, registration methods are used to align the attacked video to the original one. However, watermark detection still fails when the quality is sufficiently decreased or when exposed to targeted attacks. Therefore, this paper proposes a novel fallback system that aims to detect the watermark when traditional methods fail. More concretely, we demonstrate that a primary watermark embedded by a traditional scheme indirectly creates a secondary watermark signal during video encoding. This secondary watermark consists of compression artifacts and is detected by the fallback system. Additionally, the proposed system incorporates video registration to cope with camcording attacks. The experimental results indicate that the fallback system has a striking increase in robustness compared to the existing methods. For example, the observed false-negative rate for targeted attacks improves from 100% to 0%. Moreover, the fallback is camcording resistant even when the traditional method combined with registration is not. In conclusion, the proposed system can be used as a fallback when traditional detection fails. [ABSTRACT FROM AUTHOR]

Steganography is the art of hiding confidential information. The main purpose of steganography is to hide a message and to make it invisible for an attacker even in the case of receiving an object where the message was hidden. Messages can be, for example, hidden in audio or video signals. Steganography methods can be divided into spatial and frequency. The first group of methods directly changes the components of the original image (pixels), therefore, they visually reduce the quality of the image. One of the most well-known and widespread methods is a discrete cosine transform (DCT). As an alternative to DCT, discrete wavelet transform (DWT) is increasingly being used. Wavelet transform is a relatively new concept. There is a trend towards using wavelets in signal processing and analysis instead of or in addition to discrete cosine transform (DCT). One of the applications of steganography in an image is the use of a watermark. The purpose of a digital watermark is to hide the watermark (a kind of identification data) inside the image so that it is not perceptible to the human eye, but also so that it is stable enough to withstand various kinds of transformations. This paper is focusing on successfully hiding information in an image with the help of a wavelet transform without a visible change to the source file. The proposed algorithm includes a single-level 2D wavelet transform based on various types of transformations such as Haara, Daubeshi and Coiflet. [ABSTRACT FROM AUTHOR]

Background: Remote photoplethysmography (rPPG) is a promising optical method for non-contact assessment of pulse rate (PR) from video recordings. In order to implement the method in real-time applications, it is necessary for the rPPG algorithms to be capable of eliminating as many distortions from the pulse signal as possible. Methods: In order to increase the degrees-of-freedom of the distortion elimination, the dimensionality of the RGB video signals is increased by the wavelet transform decomposition using the generalized Morse wavelet. The proposed Continuous-Wavelet-Transform-based Sub-Band rPPG method (SB-CWT) is evaluated on the 101 publicly available RGB facial video recordings and corresponding reference blood volume pulse (BVP) signals taken from the MMSE-HR database. The performance of the SB-CWT is compared with the performance of the state-of-the-art Sub-band rPPG (SB). Results: Median signal-to-noise ratio (SNR) for the proposed SB-CWT ranges from 6.63 to 10.39 dB and for the SB from 4.23 to 6.24 dB. The agreement between the estimated PRs from rPPG pulse signals and the reference signals in terms of the coefficients of determination ranges from 0.81 to 0.91 for SB-CWT and from 0.41 to 0.47 for SB. All the correlation coefficients are statistically significant (p < 0.001). The Bland-Altman plots show that mean difference range from 5.37 to 1.82 BPM for SB-CWT and from 22.18 to 18.80 BPM for SB. Discussion: The results show that the proposed SB-CWT outperforms SB in terms of SNR and the agreement between the estimated PRs from RGB video signals and PRs from the reference BVP signals. [ABSTRACT FROM AUTHOR]

Nowadays, when we have a strong presence of computers in our everyday lives the protection of electronically processed information using digital machines becomes very important. It is related to accidental formation of electromagnetic fields which distinctive features are intrinsically linked to the nature of processed information. Although many protective measures are taken - from technical to organisational - this phenomenon still poses a great threat. Therefore, methods supporting information protection against the so-called electromagnetic infiltration are still being developed. In addition, these methods would support security of data processing and perhaps replace some of the currently used solutions characterised by high costs of implementation, or a small degree of ergonomics. Conducted research deals both with an analogue VGA and digital DVI standards. The article presents the results of the research concerning possibilities of manipulating the level of video signal (black colour intensity) and its influence on the quality of displayed images, that is, on the effectiveness of electromagnetic infiltration. [ABSTRACT FROM AUTHOR]

Structurally random matrices (SRMs) are a practical alternative to fully random matrices (FRMs) when generating compressive sensing measurements because of their computational efficiency and their universality with respect to the sparsifying basis. In this work we derive the statistical distribution of compressive measurements generated by various types of SRMs, as a function of the signal properties. We show that under a wide range of conditions, that distribution is a mixture of asymptotically multi-variate normal components. We point out the implications for quantization and coding of the measurements and discuss design considerations for measurements transmission systems. Simulations on real-world video signals confirm the theoretical findings and show that the signal randomization of SRMs yields a dramatic improvement in quantization properties. [ABSTRACT FROM AUTHOR]

This paper presents a parallax-robust video stitching technique for timely synchronized surveillance video. An efficient two-stage video stitching procedure is proposed in this paper to build wide Field-of-View (FOV) videos for surveillance applications. In the stitching model calculation stage, we develop a layered warping algorithm to align the background scenes, which is location-dependent and turned out to be more robust to parallax than the traditional global projective warping methods. On the selective seam updating stage, we propose a change-detection based optimal seam selection approach to avert ghosting and artifacts caused by moving foregrounds. Experimental results demonstrate that our procedure can efficiently stitch multi-view videos into a wide FOV video output without ghosting and noticeable seams. [ABSTRACT FROM AUTHOR]

The formation and structure of complete composite binary offset carrier (CBOC) modulated signals are analyzed as applied to the new-generation satellite radio navigation systems (SRNSs) (in particular, the European Galileo and, presumably, Chinese BeiDou/Compass systems). The block diagram of the CBOC-signal satellite transmitter based on the coherent adaptive subcarrier modulation (CASM) algorithm is discussed. Analytical expressions for the correlation functions (CFs) characterizing the single elements of the modulating function (MF) of CBOC signals are derived at arbitrary fractional coefficients. The CFs of the single elements of the MFs of CBOC signals are represented graphically and their properties are discussed. The Fourier transforms of CFs are used to calculate the energy spectra (power spectral densities) of the single elements of the MFs of CBOC signals. CF features and the energy spectra of CBOC signals are analyzed using signals with CBOC(6, 1, a/11) modulation, which are typical of the European Galileo and Chinese BeiDou/Compass systems at the carrier frequency f = 1575.42 MHz and the fractional coefficient a = 1. [ABSTRACT FROM AUTHOR]

Current video surveillance systems are not designed to raise an automatic alert in case of situations that put people lives at risk such as accidents, assaults and terrorism among others. This is due to the fact that these systems are not able to analyze huge amounts of video signals at higher processing speed where these signals come from cameras installed in the worldwide network. Faced with this situation, scientific communities are combining efforts to design algorithms and hardware to accelerate the processing of video signals. However, most of the methods proposed to date are too complex to be implemented in hardware at the place where the video camera is installed. In this paper, we report a significantly reduced novel feature set to design an analysis algorithm of significant less complexity which recognizes human actions from video sequences. The proposed method is based on the natural domain knowledge of the human figure such as proportions of the human body and foot positions. The analysis is characterized by working on sub-sequences of the entire video signals, processing a small fragment of the whole image, estimating the location of the region of interest, using simple operations (sum, subtraction, multiplications, divisions), extracting a reduced number of features per frame (6 features), and using a combination of four linear classifiers (one perceptron and three support vector machines) with a hierarchical structure. The method is evaluated on two of the datasets cited in the human action recognition literature, the Weizmann and the UIUC datasets. Results show that for the case of the Weizmann dataset, the correct classification rate (CCR) is 99.95% when the LOOCV Protocol is used and 98.38% for the case of Protocol 60–40, which is comparable or even higher than that of current state-of-the-art methods. Confusion matrices were also obtained for the UIUC dataset, where the obtained CCR is 100% for the case of the LOOCV Protocol and 99.35% when Protocol 60–40 is used. The experimental results are promising with much fewer features (between 85 and 113 times less features), compared with other methods, and the possibility of processing more than 200 fps. [ABSTRACT FROM AUTHOR]

Abstract: The different aspects of sensors integration, and specifically that of a Mass Spectrometer (MS) with audio and video signals, are investigated for detecting and monitoring indoor fire events. The present study focuses on comparing the capabilities of a variety of chemical sensors, on answering technical challenges in regard to the integration of chemical, audio and video signals and on discussing integration issues for potential field applications. Controlled, small scale fire experiments were carried out in the laboratory. A commercial MS coupled with an in-house developed Pulsed Sampling System (PSS), was used for on-line sampling and near real-time monitoring of the evolved volatiles. The detection limit of PSS-MS was found to be 150ppbv and its linearity was confirmed up to 10ppmv using benzene gas standards. The profiles of ions with m/z 57, 78, 91 and 106, corresponding to indicative Volatile Organic Compounds (VOCs) of the fire event, were recorded and compared with the concentration profiles of CO2, CO, O2, NO and H/C (C3H8), acquired by the gas sensors of a commercial exhaust gas analyzer. Audio and video signals were recorded by a microphone and a visual camera, simultaneously, with PSS-MS data. Two types of fire experiments were performed in order to simulate field conditions: (a) direct fire monitoring, in case of unobstructed direct fire view and (b) indirect fire monitoring through reflection of audio and video signals on metallic surfaces, for simulating obstacles preventing direct fire view. The information derived by audio and video signals reaffirmed the chemical detection inferences for both types of fire experiments, thus increasing the credibility of each individual method. Occasionally, video, audio and chemical information were complementary, thus counterbalancing the detection limitations of the individual methods. The integrated approach of combining MS data with audio and video signals appears to be a promising method in safety and security applications, where reliable, early detection and real-time monitoring is necessary. [Copyright &y& Elsevier]

Devices powered by electricity become sources of electromagnetic emissions in the course of their operation. In the case of devices oriented to process information, these emissions can have a character of revealing emissions, i.e., those whose reception and analysis allow for remote reconstruction of related data. The best known example of this phenomenon is the formation of revealing emissions during the operation of imaging devices: monitors, projectors or printers. Increasingly more often, these components are used for communication in the form of videoconferences with other network users. The article presents the result of tests and analyses of threats related to the use of such solutions (monitors, personal computers, VoIP terminals) for the confidentiality of conversations and the data presented during them. The focus is on video signals; however, the potential possibilities of revealing speech signals were also indicated. Such phenomenon causes a huge threat to data confidentiality because the combination of graphics and sound can undoubtedly contain much more information about the protected data than just graphics or sound separately. The presented results of analyses apply to graphic data, possibilities of non-invasive acquisition of such data, similarity of images and of patterns and reconstructed image and image recognition. The results indicate that there is still a risk of loss of data confidentiality due to a phenomenon of an electromagnetic leakage, and specialized instrumentation is not required for its interception under favorable circumstances. This may particularly apply to audio data that may be accidentally received by home radio receivers. In particular, the presented results of analyses apply to a Special Issue of Symmetry which is characterized by security and privacy in communication systems and networks, signal processing, video and image processing, multimedia communications and electromagnetic compatibility. All these scientific and technical areas have either symmetrical or asymmetrical approaches, and they have to be taken into consideration as a whole in order to choose the best combinations to protect processed information. [ABSTRACT FROM AUTHOR]

• Inaccuracy of seizure detection may cause adverse effects to treatment assessment. • This multimodal system detected various nocturnal seizure types of a single patient. • This system has a potential to differentiate motor seizures based on video and sound. • Further validation with a more diverse patient population has been started. In this proof-of-concept investigation, we demonstrate a marker-free video-based method to detect nocturnal motor seizures across a spectrum of motor seizure types, in a nighttime setting with a single adult female with refractory epilepsy. In doing so, we further explore the intermediate biosignals, visually mapping seizure "fingerprints" to seizure types. The method is designed to be flexible enough to generalize to unseen data, and shows promising performance characteristics for low-cost seizure detection and classification. The dataset contained recordings from 27 recorded nights. Seizure events were observed in 22 of these nights, with 36 unequivocally confirmed seizures. Each seizure was classified by an expert epileptologist according to both the ILAE 2017 standard and the Lüders semiological classification guidelines, yielding 5 of the ILAE-recognized seizure types and 7 distinct seizure semiologies. Evaluation was based on inference of motion, oscillation, and sound signals extracted from the recordings. The model architecture consisted of two feature extraction and event determination layers and one thresholding layer, establishing a simple framework for multimodal seizure analysis. Training of the optimal parameters was done by randomly resampling the event hits for each signal, and choosing a threshold that kept an expected 90 % sensitivity for the sample distribution. With the cut-off values selected, statistical performance was calculated for two target seizure groups: those containing a clonic component, and those containing a tonic component. When tuned to 90 % sensitivity, the system achieved a very low false discovery rate of 0.038/hour when targeting seizures with a clonic component, and a clinically-relevant rate of 1.02/hour when targeting seizures with a tonic component. These results indicate a sensitive method for detecting various nocturnal motor seizure types, and a high potential to differentiate motor seizures based on their video and audio signal characteristics. Paired with the low cost of this technique, both cost savings and improved quality of care might be achieved through further development and commercialization of this method. [ABSTRACT FROM AUTHOR]

Cellular micromotion—a tiny movement of cell membranes on the nm-µm scale—has been proposed as a pathway for inter-cellular signal transduction and as a label-free proxy signal to neural activity. Here we harness several recent approaches of signal processing to detect such micromotion in video recordings of unlabeled cells. Our survey includes spectral filtering of the video signal, matched filtering, as well as 1D and 3D convolutional neural networks acting on pixel-wise time-domain data and a whole recording respectively. [ABSTRACT FROM AUTHOR]