Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network

Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV) workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube demo at this: https://www.youtube.com/watch?v=zYypJPJipY

Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network

Disguised face identification (DFI) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises. This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification. Since the training of deep learning architectures relies on large annotated datasets, two annotated facial key-points datasets are introduced. The effectiveness of the facial keypoint detection framework is presented for each keypoint. The superiority of the key-point detection framework is also demonstrated by a comparison with other deep networks. The effectiveness of classification performance is also demonstrated by comparison with the state-of-the-art face disguise classification methods.Comment: To Appear in the IEEE International Conference on Computer Vision Workshops (ICCVW) 201

ScatterNet Hybrid Frameworks for Deep Learning

Image understanding is the task of interpreting images by effectively solving the individual tasks of object recognition and semantic image segmentation. An image understanding system must have the capacity to distinguish between similar looking image regions while being invariant in its response to regions that have been altered by the appearance-altering transformation. The fundamental challenge for any such system lies within this simultaneous requirement for both invariance and specificity. Many image understanding systems have been proposed that capture geometric properties such as shapes, textures, motion and 3D perspective projections using filtering, non-linear modulus, and pooling operations. Deep learning networks ignore these geometric considerations and compute descriptors having suitable invariance and stability to geometric transformations using (end-to-end) learned multi-layered network filters. These deep learning networks in recent years have come to dominate the previously separate fields of research in machine learning, computer vision, natural language understanding and speech recognition. Despite the success of these deep networks, there remains a fundamental lack of understanding in the design and optimization of these networks which makes it difficult to develop them. Also, training of these networks requires large labeled datasets which in numerous applications may not be available. In this dissertation, we propose the ScatterNet Hybrid Framework for Deep Learning that is inspired by the circuitry of the visual cortex. The framework uses a hand-crafted front-end, an unsupervised learning based middle-section, and a supervised back-end to rapidly learn hierarchical features from unlabelled data. Each layer in the proposed framework is automatically optimized to produce the desired computationally efficient architecture. The term `Hybrid' is coined because the framework uses both unsupervised as well as supervised learning. We propose two hand-crafted front-ends that can extract locally invariant features from the input signals. Next, two ScatterNet Hybrid Deep Learning (SHDL) networks (a generative and a deterministic) were introduced by combining the proposed front-ends with two unsupervised learning modules which learn hierarchical features. These hierarchical features were finally used by a supervised learning module to solve the task of either object recognition or semantic image segmentation. The proposed front-ends have also been shown to improve the performance and learning of current Deep Supervised Learning Networks (VGG, NIN, ResNet) with reduced computing overhead

Ebola Hemorrhagic Fever: Recent Update On Disease Status, Current Therapies And Advances In Treatment

Swiftly growing viruses are a major intimidation to human health. Such viruses are extremely pathogenic like Ebola virus, influenza virus, HIV virus, Zika virus etc . Ebola virus, a type of Filovirus, is an extremely infectious, single-stranded ribonucleic acid virus that infects both humans and apes, prompting acute fever with hemorrhagic syndrome. The high infectivity, severity and mortality of Ebola has plagued the world for the past fifty years with its first outbreak in 1976 in Marburg, Germany, and Frankfurt along with Belgrade and Serbia. The world has perceived about 28,000 cases and over 11,000 losses. The high lethality of Ebola makes it a candidate for use in bioterrorism thereby arising more concern. New guidelines have been framed for providing best possible care to the patients suffering from Ebola virus i.e Grading of Recommendation Assessment, Development And Evaluation (GRADE) methodology to develop evidence-based strategy for the treatment in future outbreak of Ebola virus. No drugs have been approved, while many potent drugs like rVSV-EBOV, Favipiravir, ZMapp are on clinical test for human safety. In this review we will discover and discuss perspective aspects that lead to the evolution of different Ebola variants as well as advances in various drugs and vaccines for treatment of the disease

Autonomous UAV for suspicious action detection using pictorial human pose estimation and classification

Visual autonomous systems capable of monitoring crowded areas and alerting the authorities in occurrence of a suspicious action can play a vital role in controlling crime rate. Previous atte mpts have been made to monitor crime using posture recognition but nothing exclusive to investigating actions of people in large populated area has been cited. In order resolve this shortcoming, we propose an autonomous unmanned aerial vehicle (UAV) visual surveillance system that locates humans in image frames followed by pose estimation using weak constraints on position, appearance of body parts and image parsing. The estimated pose, represented as a pictorial structure, is flagged using the proposed Hough Orientation Calculator (HOC) on close resemblance with any pose in the suspicious action dataset. The robustness of the system is demonstrated on videos recorded using a UAV with no prior knowledge of background, lighting or location and scale of the human in the image. The system produces an accuracy of 71% and can also be applied on various other video sources such as CCTV camera

Role of Protein Kinase C in Diabetic Complications

Diabetes is the most common and systemic disorder associated with hyperglycemia which is the significant factor in the development of micro- and macrovascular changes. Many mechanistic approaches i.e. activation of Protein kinase C, glycation end products production, hexosamine pathway and polyol pathway induce cellular damage and lead to the development of diabetic complications like nephropathy, neuropathy, retinopathy, and myopathy. One of the adverse effects of long-lasting hyperglycemia is activation of PKC (intracellular signaling enzyme) and has become a field of great research interest. Hence, in this review special emphasis is placed on microvascular complications which are due to activation of PKC. Clinical trials have also been conducted using selective PKC inhibitors and have shown positive results against hyperglycemia

Histone Deacetylase Inhibitors As Potential Therapeutic Agents For Various Disorders

Epigenetic modification acetylation or deacetylation of histone considered as an important element in various disorders. Histone acetyltransferases (HATs) and histone deacetylases (HDACs) are the enzymes which catalyse the acetylation and deacetylation of histone respectively. It helps in regulating the condensation of chromatin and transcription of genes. Lysine acetylation and deacetylation present on the nucleosomal array of histone is the key factor for gene expression and regulation in a normal working living cell. Modification in histone protein will lead to the development of cancer and can cause various neurodegenerative disorders. To safeguard the cells or histone proteins from these diseases histone deacetylase inhibitors are used. In this review, the main focus is upon the role of histone deacetylases inhibitors in various diseases

Automatic Methods for Human Embryo Component Extraction

In this thesis, we proposed two novel methods for blastomere extraction and trophectoderm segmentation in an attempt to aid physicians in determining embryo’s viability. Accurate assessment of embryo’s viability can play a vital role towards optimization of in-vitro fertilisation (IVF) treatment outcomes. The first proposed automatic method is developed to identify blastomeres in human embryo HMC (Hoffman Modulation Contrast) images of day-1 to day-2. Our algorithm applies isoperimetric graph partitioning, followed by a novel region merging algorithm to approximate blastomeres positions. Ellipsoidal models are then used to approximate the shape and the size of each blastomere. The proposed algorithm is evaluated on a dataset of 40 embryo images and it exhibits an average blastomere extraction accuracy of 80%. The second method segments Trophectoderm (TE) regions in embryos of day-5 (also known as blastocysts) by first eliminating the inhomogeneities of the blastocysts surface using Retinex theory. A level set algorithm is then used to segment TE regions. We have tested our method on a dataset of 85 images and have achieved a segmentation accuracy of 85% for grade A, 89% for grade B and 92% for grade C