Addressing the growing need for immediate sports activities analysis and automatic content material, a new smart system detects basketball highlights in real time. This deep studying framework makes use of a custom-tr...
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ISBN:
(数字)9798331597092
ISBN:
(纸本)9798331597108
Addressing the growing need for immediate sports activities analysis and automatic content material, a new smart system detects basketball highlights in real time. This deep studying framework makes use of a custom-trained YOLOv8 model for specific detection and tracking of the basketball. Key capabilities consist of automated camera movement and viewport smoothing, improved by speed-based motion prediction and confidence-aware monitoring. Adaptive stabilization minimizes jitter whilst following the movement. A sliding-window buffer statistics video before and after huge occasions, such as successful shots, to generate entire highlight clips right away. tested on live and recorded footage, the machine operates at real-time pace and dynamically crops videos to a mobile-friendly 9:16 aspect ratio. The consequences demonstrate robust overall performance in automating highlight technology, appreciably decreasing the need for guide enhancing. This enables instantaneous replay creation useful for broadcasting, coaching analysis, and improving fan interaction.
The rapid expansion of data poses a significant challenge for analyzing sentiments. The importance of user-generated reviews highlights the need to carefully curate and evaluate text data to extract opinions. This res...
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The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and ...
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While existing methods for 3D face reconstruction from in-the-wild images excel at recovering the overall face shape, they commonly miss subtle, extreme, asymmetric, or rarely observed expressions. We improve upon the...
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ISBN:
(数字)9798350353006
ISBN:
(纸本)9798350353013
While existing methods for 3D face reconstruction from in-the-wild images excel at recovering the overall face shape, they commonly miss subtle, extreme, asymmetric, or rarely observed expressions. We improve upon these meth-ods with SMIRK (Spatial Modeling for Image-based Reconstruction of Kinesics), which faithfully reconstructs expres-sive 3D faces from images. We identify two key limitations in existing methods: shortcomings in their self-supervised training formulation, and a lack of expression diversity in the training images. For training, most methods employ differentiable rendering to compare a predicted face mesh with the input image, along with a plethora of additional loss functions. This differentiable rendering loss not only has to provide supervision to optimize for 3D face geom-etry, camera, albedo, and lighting, which is an ill-posed optimization problem, but the domain gap between ren-dering and input image further hinders the learning pro-cess. Instead, SMIRK replaces the differentiable rendering with a neural rendering module that, given the ren-dered predicted mesh geometry, and sparsely sampled pix-els of the input image, generates a face image. As the neural rendering gets color information from sampled im-age pixels, supervising with neural rendering-based reconstruction loss can focus solely on the geometry. Further it enables us to generate images of the input identity with varying expressions while training. These are then utilized as input to the reconstruction model and used as supervision with ground truth geometry. This effectively augments the training data and enhances the generalization for di-verse expressions. Our qualitative, quantitative and partic-ularly our perceptual evaluations demonstrate that SMIRK achieves the new state-of-the art performance on accurate expression reconstruction. For our method's source code, demo video and more, please visit our project webpage: https://***/smirk/.
Object grasping is a complex task that requires high environmental awareness. While vision generally provides highly detailed environmental information, light changes, object transparency, camera resolution, and other...
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ISBN:
(数字)9798350371628
ISBN:
(纸本)9798350371635
Object grasping is a complex task that requires high environmental awareness. While vision generally provides highly detailed environmental information, light changes, object transparency, camera resolution, and other factors such as occlusion and clutter affect its perception of object pose. Due to these limitations, there may be some deviation between the estimated and actual object pose in unstructured environments. The use of compliant tactile sensors relaxes the requirement of strict finger position planning while providing essential information regarding contact with the target object. Therefore, under positional uncertainty, the robotic system may use compliant tactile sensors to perform multiple attempts before a successful grasp. In the present paper, we investigate using reinforcement learning and compliant tactile sensors to provide adaptive grasping under pose uncertainty. Here, we identify a policy that models an object position estimation error while minimizing the exploratory sensor contact before obtaining a grasp. Our method was able to perform a successful grasp while reducing the number of attempts from an average of five to an average of two per episode.
Object detection in satellite imagery is challenging due to small object scale. Traditional one-shot and region proposal methods struggle with accuracy and computational costs. We propose a novel deep reinforcement le...
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DynamicvisionCore is a novel predictive object tracking framework designed for real-time robotics applications. The system integrates YOLOv8 for object detection and DeepSORT for multi-object tracking, ensuring high a...
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Face detection and recognition is an extensively researched topic in Artificial Intelligence. The use of AI in detection and mapping of faces or any objects can reduce the time spent in video auditing. A face recognit...
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Referring Video Object Segmentation (R-VOS) methods face challenges in maintaining consistent object segmentation due to temporal context variability and the presence of other visually similar objects. We propose an e...
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Detecting defects in fabric is a crucial step in the textile industry. The intricate nature of textile structures often poses a challenge for the automated identification of fabric damage. Fabric Defect Detection (FDD...
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