Biblio

Recently, smart video surveillance at the edge has become a trend in developing security applications since edge computing enables more image processing tasks to be implemented on the decentralised network note of the surveillance system. As a result, many security applications such as behaviour recognition and prediction, employee safety, perimeter intrusion detection and vandalism deterrence can minimise their latency or even process in real-time when the camera network system is extended to a larger degree. Technically, human detection is a key step in the implementation of these applications. With the advantage of high detection rates, deep learning methods have been widely employed on edge devices in order to detect human objects. However, due to their high computation costs, it is challenging to apply these methods on resource limited edge devices for real-time applications. Inspired by the You Only Look Once (YOLO), residual learning and Spatial Pyramid Pooling (SPP), a novel form of real-time human detection is presented in this paper. Our approach focuses on designing a network structure so that the developed model can achieve a good trade-off between accuracy and processing time. Experimental results show that our trained model can process 2 FPS on Raspberry PI 3B and detect humans with accuracies of 95.05 % and 96.81 % when tested respectively on INRIA and PENN FUDAN datasets. On the human COCO test dataset, our trained model outperforms the performance of the Tiny-YOLO versions. Additionally, compare to the SSD based L-CNN method, our algorithm achieves better accuracy than the other method.

Cyber-attacks toward cyber-physical systems are one of the main concerns of smart grid's operators. However, many of these cyber-attacks, are toward unmanned substations where the cyber-attackers needs to be close enough to substation to malfunction protection and control systems in substations, using Electromagnetic signals. Therefore, in this paper, a new threat detection algorithm is proposed to prevent possible cyber-attacks toward unmanned substations. Using surveillance camera's streams and based on You Only Look Once (YOLO) V3, suspicious objects in the image are detected. Then, using Intersection over Union (IOU) and Generalized Intersection Over Union (GIOU), threat distance is estimated. Finally, the estimated threats are categorized into three categories using color codes red, orange and green. The deep network used for detection consists of 106 convolutional layers and three output prediction with different resolutions for different distances. The pre-trained network is transferred from Darknet-53 weights trained on 80 classes.

To bring a uniform development platform which seamlessly combines hardware components and software architecture of various developers across the globe and reduce the complexity in producing robots which help people in their daily ergonomics. ROS has come out to be a game changer. It is disappointing to see the lack of penetration of technology in different verticals which involve protection, defense and security. By leveraging the power of ROS in the field of robotic automation and computer vision, this research will pave path for identification of suspicious activity with autonomously moving bots which run on ROS. The research paper proposes and validates a flow where ROS and computer vision algorithms like YOLO can fall in sync with each other to provide smarter and accurate methods for indoor and limited outdoor patrolling. Identification of age,`gender, weapons and other elements which can disturb public harmony will be an integral part of the research and development process. The simulation and testing reflects the efficiency and speed of the designed software architecture.

Self-describing the content of a video is an elementary problem in artificial intelligence that joins computer vision and natural language processing. Through this paper, we propose a single system which could carry out video analysis (Object Detection and Captioning) at a reduced time and memory complexity. This single system uses YOLO (You Look Only Once) as its base model. Moreover, to highlight the importance of using transfer learning in development of the proposed system, two more approaches have been discussed. The rest one uses two discrete models, one to extract continuous bag of words from the frames and other to generate captions from those words i.e. Language Model. VGG-16 (Visual Geometry Group) is used as the base image decoder model to compare the two approaches, while LSTM is the base Language Model used. The Dataset used is Microsoft Research Video Description Corpus. The dataset was manually modified to serve the purpose of training the proposed system. Second approach which uses transfer learning proves to be the better approach for development of the proposed system.