“…Some of these board includes Nvidia Jetson Nano, Nvidia Xavier, Google Coral, AWS DeepLens, and Intel AI-Stick. Authors in [34][35] proposed a raspberry pi-based edge computing system to detect thermal objects. Sen Cao et al [36] developed a roadside object detector using KITTI dataset [37] by training an efficient and lightweight neural network on Nvidia Jetson TX2 embedded GPU.…”
Section: B Object Detection On Edge Devicesmentioning
This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale C3I Thermal Automotive dataset comprising of > 35,000 distinct frames is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is recorded from a lost-cost yet effective uncooled LWIR thermal camera, mounted standalone and on an electric vehicle to minimize mechanical vibrations. state-of-the-art YOLO-v5 networks variants are trained using four different public datasets as well newly acquired local dataset for optimal generalization of DNN by employing SGD optimizer. The effectiveness of trained networks is validated on extensive test data using various quantitative metrics which include precision, recall curve, mean average precision, and frames per second. The smaller network variant of YOLO is further optimized using TensorRT inference accelerator to explicitly boost the frames per second rate. Optimized network engine increases the frames per second rate by 3.5 times when testing on low power edge devices thus achieving 11 fps on Nvidia Jetson Nano and 60 fps on Nvidia Xavier NX development boards.
“…Some of these board includes Nvidia Jetson Nano, Nvidia Xavier, Google Coral, AWS DeepLens, and Intel AI-Stick. Authors in [34][35] proposed a raspberry pi-based edge computing system to detect thermal objects. Sen Cao et al [36] developed a roadside object detector using KITTI dataset [37] by training an efficient and lightweight neural network on Nvidia Jetson TX2 embedded GPU.…”
Section: B Object Detection On Edge Devicesmentioning
This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale C3I Thermal Automotive dataset comprising of > 35,000 distinct frames is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is recorded from a lost-cost yet effective uncooled LWIR thermal camera, mounted standalone and on an electric vehicle to minimize mechanical vibrations. state-of-the-art YOLO-v5 networks variants are trained using four different public datasets as well newly acquired local dataset for optimal generalization of DNN by employing SGD optimizer. The effectiveness of trained networks is validated on extensive test data using various quantitative metrics which include precision, recall curve, mean average precision, and frames per second. The smaller network variant of YOLO is further optimized using TensorRT inference accelerator to explicitly boost the frames per second rate. Optimized network engine increases the frames per second rate by 3.5 times when testing on low power edge devices thus achieving 11 fps on Nvidia Jetson Nano and 60 fps on Nvidia Xavier NX development boards.
“…The authors' judgment is based on listening to some voice samples produced for each pre-trained algorithm from their separate GitHub repositories, as well as reviews from the literature. The paper in [104] proposed the implementation of a full end-to-end edge computing system capable of reliably classifying hand motions acquired from thermography. A dataset from a thermography of 321 photos were developed, comprising of 321 thermal images for each sign language digit.…”
Section: Use Case 2: Intelligent Multimedia Processing On Edge For Hu...mentioning
confidence: 99%
“…This can serve as a comprehensive reference and point researchers and readers towards further and future works. Use Case 2: Intelligent Multimedia Processing on the Edge for Human Computer Interaction (HCI) and Health [99][100][101][102][103][104][105][106][107][108][109][110][111] The remainder of the paper is structured as follows: Section 2 provides initial discussions and background information for the edge paradigm, multimedia information processing and artificial intelligence. The next five sections provide in-depth discussions on five areas of interest.…”
The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. The motivation of this paper was to survey these new paradigms for multimedia and edge information processing from several technological perspectives including: (1) multimedia analytics on the edge empowered by AI; (2) multimedia streaming on the intelligent edge; (3) multimedia edge caching and AI; (4) multimedia services for edge AI; and (5) hardware and devices for multimedia on edge intelligence. The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing.
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