Weapon detection in CCTV camera surveillance videos is a challenging task and its importance is increasing because of the availability and easy access of weapons in the market. This becomes a big problem when weapons go into the wrong hands and are often misused. Advances in computer vision and object detection are enabling us to detect weapons in live videos without human intervention and, in turn, intelligent decisions can be made to protect people from dangerous situations. In this article, we have developed and presented an improved real-time weapon detection system that shows a higher mean average precision (mAP) score and better inference time performance compared to the previously proposed approaches in the literature. Using a custom weapons dataset, we implemented a state-of-the-art Scaled-YOLOv4 model that resulted in a 92.1 mAP score and frames per second (FPS) of 85.7 on a high-performance GPU (RTX 2080TI). Furthermore, to achieve the benefits of lower latency, higher throughput, and improved privacy, we optimized our model for implementation on a popular edge-computing device (Jetson Nano GPU) with the TensorRT network optimizer. We have also performed a comparative analysis of the previous weapon detector with our presented model using different CPU and GPU machines that fulfill the purpose of this work, making the selection of model and computing device easier for the users for deployment in a real-time scenario. The analysis shows that our presented models result in improved mAP scores on high-performance GPUs (such as RTX 2080TI), as well as on low-cost edge computing GPUs (such as Jetson Nano) for weapon detection in live CCTV camera surveillance videos.

In wireless networks, due to limited bandwidth and packet losses, seamless and ubiquitous delivery of high-quality video streaming services is a big challenge for the operators. In order to improve the process of online video quality monitoring, the presence of no reference (NR) objective video quality assessment (VQA) methods is required. In some networks, the video decoder on the reception side adopts a mechanism in which last correctly received frame is frozen and displayed on video display terminal until the next correct frame is received. This phenomenon, employed as an error concealment technique, can cause a perceptual jerkiness on the video display terminal. In this paper, we have proposed an enhanced model of objective VQA based on the estimation of jerkiness. A study of three contemporary NR methods, used for objective VQA and online monitoring of videos, has been included along with subjective VQA tests. The subjective tests were performed for a set of video sequences with specific spatial and temporal information. The proposed NR method is based on our careful observations from the subjective test results and our main focus is to cater the effect of multiple frame freeze impairments in video steaming. Comparison with other NR methods shows that the proposed method performs better, in terms of estimating the impact of multiple frame freezing impairments, and has more affinity with the subjective test results.