Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

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Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360°video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360°video from the cross-lab tests (involving ten laboratories and more than 300 participants) carried out by the Immersive Media Group (IMG) of the Video Quality Experts Group (VQEG). These tests were addressed to assess and validate subjective evaluation methodologies for 360°video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360°sequences. The following factors' influences were also analyzed: assessment methodology, sequence Manuscript received

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In addition to traditional Quality of Service (QoS), Quality of Experience (QoE) poses a real challenge for Internet service providers, audio-visual services, broadcasters and new Over-The-Top (OTT) services. Therefore, objective audio-visual metrics are frequently being dedicated in order to monitor, troubleshoot, investigate and set benchmarks of content applications working in real-time or off-line. The concept proposed here, Monitoring of Audio Visual Quality by Key Performance Indicators (MOAVI), is able to isolate and focus investigation, set-up algorithms, increase the monitoring period and guarantee better prediction of perceptual quality. MOAVI artefacts Key Performance Indicators (KPI) are classified into four categories, based on their origin: capturing, processing, transmission, and display. In the paper, we present experiments carried out over several steps with four experimental set-ups for concept verification. The methodology takes into the account annoyance visibility threshold. The experimental methodology is adapted from International Telecommunication Union -Telecommunication Standardization Sector (ITU-T)The research leading to these results has received funding from the European Regional Development Fund

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