Feasibility Study of Solar Powered Unmanned Aerial Vehicle

High Altitude Long Endurance Unmanned Aerial Vehicles (HALE UAVs) could provide an improved service and/or flexibility at a reduced cost over existing systems for a vast number of civil patrol and surveillance applications. This document looks into the Feasibility and Conceptual Design of Solar Powered UAV for HALE applications. It mentions the advancements in technology of the components required to build an efficient solar powered UAV. It also provides a preliminary design methodology that can be adopted for the conceptual design of Solar Powered UAV. It also emphasizes the Aerodynamic difficulties that are faced in HALE configurations.

Association with Temperature Variability and Physical Activity, Sedentary Behavior, and Sleep in a Free-Living Population

The present study examines the temperature variability in physical activity (PA), sedentary behavior (SB), and sleep in a free-living population. A representative sample of 1235 adults (ages 21–70) from Iowa, U.S.A., wore a SenseWear Mini Armband (SWA) for a randomly assigned day. Koppen’s weather climate classification was used to precisely classify the temperature: cold (−13 to 32 °F), cool (32 to 50 °F), mild (50 to 64 °F), warm (64 to 73 °F), and hot (73 to 95 °F). The main effect of three-way ANOVA (age × gender × temperature) had differences for SB and sleep, with older adults having higher levels than younger adults (p < 0.05). However, moderate to vigorous PA (MVPA) did not vary systematically by age or gender, and contrary to expectations, the main effect of the weather was not significant for MVPA (p > 0.05). Participants spent more time participating in PA at cold than at hot temperatures. The results clarify the impact of temperature on shaping PA and SB patterns in adults. The variable impacts and differential patterns by age suggest that weather should be considered when interpreting differences in PA patterns in research or surveillance applications.

Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman filter to enhance the perception performance. In addition, UAV path planning for a surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.

Object Detection in Deep Surveillance

Object detection is a key ability required by most computer visions and surveillance applications. Pedestrian detection is a key problem in surveillance, with several applications such as person identification, person count and tracking. The number of techniques to identifying pedestrians in images has gradually increased in recent years, even with the significant advances in the state-of-the-art deep neural network-based framework for object detection models. The research in the field of object detection and image classification has made a stride in the level of accuracy greater than 99% and the level of granularity. A powerful Object detector, specifically designed for high-end surveillance applications, is needed that will not only position the bounding box and label it but will also return their relative positions. The size of these bounding boxes can vary depending on the object and it interacts with the physical world. To address these requirements, an extensive evaluation of the state-of-the-art algorithms has been performed in this paper. The work presented in this paper performs detections on MOT20 dataset using various algorithms and testing on a custom dataset recorded in our organization premises using an Unmanned Aerial Vehicle (UAV). The experimental analysis has been performed on Faster-RCNN, SSD and YOLO models. The Yolov5 model is found to outperform all the other models with 61% precision and 44% of F measure value.

Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System

Person Re-Identification is an essential task in computer vision, particularly in surveillance applications. The aim is to identify a person based on an input image from surveillance photographs in various scenarios. Most Person re-ID techniques utilize Convolutional Neural Networks (CNNs); however, Vision Transformers are replacing pure CNNs for various computer vision tasks such as object recognition, classification, etc. The vision transformers contain information about local regions of the image. The current techniques take this advantage to improve the accuracy of the tasks underhand. We propose to use the vision transformers in conjunction with vanilla CNN models to investigate the true strength of transformers in person re-identification. We employ three backbones with different combinations of vision transformers on two benchmark datasets. The overall performance of the backbones increased, showing the importance of vision transformers. We provide ablation studies and show the importance of various components of the vision transformers in re-identification tasks.