Automated recognition of human activities or actions has great significance as it incorporates wide-ranging applications, including surveillance, robotics, and personal health monitoring. Over the past few years, many computer vision-based methods have been developed for recognizing human actions from RGB and depth camera videos. These methods include space-time trajectory, motion encoding, key poses extraction, space-time occupancy patterns, depth motion maps, and skeleton joints. However, these camera-based approaches are affected by background clutter and illumination changes and applicable to a limited field of view only. Wearable inertial sensors provide a viable solution to these challenges but are subject to several limitations such as location and orientation sensitivity. Due to the complementary trait of the data obtained from the camera and inertial sensors, the utilization of multiple sensing modalities for accurate recognition of human actions is gradually increasing. This paper presents a viable multimodal feature-level fusion approach for robust human action recognition, which utilizes data from multiple sensors, including RGB camera, depth sensor, and wearable inertial sensors. We extracted the computationally efficient features from the data obtained from RGB-D video camera and inertial body sensors. These features include densely extracted histogram of oriented gradient (HOG) features from RGB/depth videos and statistical signal attributes from wearable sensors data. The proposed human action recognition (HAR) framework is tested on a publicly available multimodal human action dataset UTD-MHAD consisting of 27 different human actions. K-nearest neighbor and support vector machine classifiers are used for training and testing the proposed fusion model for HAR. The experimental results indicate that the proposed scheme achieves better recognition results as compared to the state of the art. The feature-level fusion of RGB and inertial sensors provides the overall best performance for the proposed system, with an accuracy rate of 97.6%.
The classification of high-resolution satellite images is an open research problem for computer vision research community. In last few decades, the Bag of Visual Word (BoVW) model has been used for the classification of satellite images. In BoVW model, an orderless histogram of visual words without any spatial information is used as image signature. The performance of BoVW model suffers due to this orderless nature and addition of spatial clues are reported beneficial for scene and geographical classification of images. Most of the image representations that can compute image spatial information as are not invariant to rotations. A rotation invariant image representation is considered as one of the main requirement for satellite image classification. This paper presents a novel approach that computes the spatial clues for the histograms of BoVW model that is robust to the image rotations. The spatial clues are calculated by computing the histograms of orthogonal vectors. This is achieved by calculating the magnitude of orthogonal vectors between Pairs of Identical Visual Words (PIVW) relative to the geometric center of an image. The comparative analysis is performed with recently proposed research to obtain the best spatial feature representation for the satellite imagery. We evaluated the proposed research for image classification using three standard image benchmarks of remote sensing. The results and comparisons conducted to evaluate this research show that the proposed approach performs better in terms of classification accuracy for a variety of datasets based on satellite images.
Visually impaired persons (VIPs) comprise a significant portion of the population, and they are present around the globe and in every part of the world. In recent times, technology proved its presence in every domain, and innovative devices assist humans in their daily lives. In this work, a smart and intelligent system is designed for VIPs to assist mobility and ensure their safety. The proposed system provides navigation in real-time using an automated voice. Though VIPs wouldn't be able to see objects in their surroundings, they can sense and visualize the roaming environment. Moreover, a web-based application is developed to ensure their safety. The user of this application can turn the on-demand function for sharing his/her location with the family while compromising privacy. Through this application, the family members of VIPs would be able to track their movement (get location and snapshots) while being at their homes. Hence, the device allows VIPs to visualize the environment and ensure their security. Such a comprehensive device was a missing link in the existing literature. The application uses MobileNet architecture due to its low computational complexity to run on low-power end devices. To assess the efficacy of the proposed system, six pilot studies have been performed that reflected satisfactory results. For object detection and recognition, a deep Convolution Neural Network (CNN) model is employed with an accuracy of 83.3%, whereas the dataset contains more than 1000 categories. Moreover, a score-based quantitative comparative analysis is performed using the supported features of devices. It is found that the proposed system has outperformed the existing devices having a total score of 9.1/10, which is 8% higher than the second-best.
Quantitative reasoning in medical decision science relies on the delineation of pathological objects. For example, evidence‐based clinical decisions regarding lung diseases require the segmentation of nodules, tumors, or cancers. Non‐small cell lung cancer (NSCLC) tends to be large sized, irregularly shaped, and grows against surrounding structures imposing challenges in the segmentation, even for expert clinicians. An automated delineation tool based on spatial analysis was developed and studied on 25 sets of computed tomography scans of NSCLC. Manual and automated delineations were compared, and the proposed method exhibited robustness in terms of the tumor size (5.32–18.24 mm), shape (spherical or irregular), contouring (lobulated, spiculated, or cavitated), localization (solitary, pleural, mediastinal, endobronchial, or tagging), and laterality (left or right lobe) with accuracy between 80% and 99%. Small discrepancies observed between the manual and automated delineations may arise from the variability in the practitioners' definitions of region of interest or imaging artifacts that reduced the tissue resolution.
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