Hand Gestures Recognition in Videos Taken with Lensless Camera

• URL: http://arxiv.org/abs/2210.08233v1
• Date: Sat, 15 Oct 2022 08:52:49 GMT
• Title: Hand Gestures Recognition in Videos Taken with Lensless Camera
• Authors: Yinger Zhang, Zhouyi Wu, Peiying Lin, Yang Pan, Yuting Wu, Liufang Zhang and Jiangtao Huangfu
• Abstract summary: This work proposes a deep learning model named Raw3dNet that recognizes hand gestures directly on raw videos captured by a lensless camera. In addition to conserving computational resources, the reconstruction-free method provides privacy protection.
• Score: 4.49422973940462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A lensless camera is an imaging system that uses a mask in place of a lens, making it thinner, lighter, and less expensive than a lensed camera. However, additional complex computation and time are required for image reconstruction. This work proposes a deep learning model named Raw3dNet that recognizes hand gestures directly on raw videos captured by a lensless camera without the need for image restoration. In addition to conserving computational resources, the reconstruction-free method provides privacy protection. Raw3dNet is a novel end-to-end deep neural network model for the recognition of hand gestures in lensless imaging systems. It is created specifically for raw video captured by a lensless camera and has the ability to properly extract and combine temporal and spatial features. The network is composed of two stages: 1. spatial feature extractor (SFE), which enhances the spatial features of each frame prior to temporal convolution; 2. 3D-ResNet, which implements spatial and temporal convolution of video streams. The proposed model achieves 98.59% accuracy on the Cambridge Hand Gesture dataset in the lensless optical experiment, which is comparable to the lensed-camera result. Additionally, the feasibility of physical object recognition is assessed. Furtherly, we show that the recognition can be achieved with respectable accuracy using only a tiny portion of the original raw data, indicating the potential for reducing data traffic in cloud computing scenarios.

Related papers

• HORT: Monocular Hand-held Objects Reconstruction with Transformers [61.36376511119355]
  Reconstructing hand-held objects in 3D from monocular images is a significant challenge in computer vision. We propose a transformer-based model to efficiently reconstruct dense 3D point clouds of hand-held objects. Our method achieves state-of-the-art accuracy with much faster inference speed, while generalizing well to in-the-wild images.
  arXiv  Detail & Related papers  (2025-03-27T09:45:09Z)
• Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration [34.18403601269181]
  DM-Calib is a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image. We introduce a new image-based representation, termed Camera Image, which losslessly encodes the numerical camera intrinsics. By fine-tuning a stable diffusion model to generate a Camera Image from a single RGB input, we can extract camera intrinsics via a RANSAC operation.
  arXiv  Detail & Related papers  (2024-11-26T09:04:37Z)
• Transientangelo: Few-Viewpoint Surface Reconstruction Using Single-Photon Lidar [8.464054039931245]
  Lidar captures 3D scene geometry by emitting pulses of light to a target and recording the speed-of-light time delay of the reflected light. conventional lidar systems do not output the raw, captured waveforms of backscattered light. We develop new regularization strategies that improve robustness to photon noise, enabling accurate surface reconstruction with as few as 10 photons per pixel.
  arXiv  Detail & Related papers  (2024-08-22T08:12:09Z)
• DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features [65.8738034806085]
  DistillNeRF is a self-supervised learning framework for understanding 3D environments in autonomous driving scenes. Our method is a generalizable feedforward model that predicts a rich neural scene representation from sparse, single-frame multi-view camera inputs.
  arXiv  Detail & Related papers  (2024-06-17T21:15:13Z)
• SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera [78.20482568602993]
  Conventional RGB cameras are susceptible to motion blur. Neuromorphic cameras like event and spike cameras inherently capture more comprehensive temporal information. Our design can enhance novel view synthesis across NeRF and 3DGS.
  arXiv  Detail & Related papers  (2024-04-10T03:31:32Z)
• Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images [65.41966114373373]
  We present an improved solution to the neural image-based rendering problem in computer vision. The proposed approach could synthesize a realistic image of the scene from a novel viewpoint at test time.
  arXiv  Detail & Related papers  (2023-11-08T08:18:23Z)
• FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow [26.528667940013598]
  Reconstruction of 3D neural fields from posed images has emerged as a promising method for self-supervised representation learning. Key challenge preventing the deployment of these 3D scene learners on large-scale video data is their dependence on precise camera poses from structure-from-motion. We propose a method that jointly reconstructs camera poses and 3D neural scene representations online and in a single forward pass.
  arXiv  Detail & Related papers  (2023-05-31T20:58:46Z)
• Fast and Lightweight Scene Regressor for Camera Relocalization [1.6708069984516967]
  Estimating the camera pose directly with respect to pre-built 3D models can be prohibitively expensive for several applications. This study proposes a simple scene regression method that requires only a multi-layer perceptron network for mapping scene coordinates. The proposed approach uses sparse descriptors to regress the scene coordinates, instead of a dense RGB image.
  arXiv  Detail & Related papers  (2022-12-04T14:41:20Z)
• Multi-task Learning for Camera Calibration [3.274290296343038]
  We present a unique method for predicting intrinsic (principal point offset and focal length) and extrinsic (baseline, pitch, and translation) properties from a pair of images. By reconstructing the 3D points using a camera model neural network and then using the loss in reconstruction to obtain the camera specifications, this innovative camera projection loss (CPL) method allows us that the desired parameters should be estimated.
  arXiv  Detail & Related papers  (2022-11-22T17:39:31Z)
• Neural Scene Representation for Locomotion on Structured Terrain [56.48607865960868]
  We propose a learning-based method to reconstruct the local terrain for a mobile robot traversing urban environments. Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the estimates the topography in the robot's vicinity. We propose a 3D reconstruction model that faithfully reconstructs the scene, despite the noisy measurements and large amounts of missing data coming from the blind spots of the camera arrangement.
  arXiv  Detail & Related papers  (2022-06-16T10:45:17Z)
• Camera Calibration through Camera Projection Loss [4.36572039512405]
  We propose a novel method to predict intrinsic (focal length and principal point offset) parameters using an image pair. Unlike existing methods, we proposed a new representation that incorporates camera model equations as a neural network in multi-task learning framework. Our proposed approach achieves better performance with respect to both deep learning-based and traditional methods on 7 out of 10 parameters evaluated.
  arXiv  Detail & Related papers  (2021-10-07T14:03:10Z)
• BARF: Bundle-Adjusting Neural Radiance Fields [104.97810696435766]
  We propose Bundle-Adjusting Neural Radiance Fields (BARF) for training NeRF from imperfect camera poses. BARF can effectively optimize the neural scene representations and resolve large camera pose misalignment at the same time. This enables view synthesis and localization of video sequences from unknown camera poses, opening up new avenues for visual localization systems.
  arXiv  Detail & Related papers  (2021-04-13T17:59:51Z)
• Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion [51.19260542887099]
  We show that self-supervision can be used to learn accurate depth and ego-motion estimation without prior knowledge of the camera model. Inspired by the geometric model of Grossberg and Nayar, we introduce Neural Ray Surfaces (NRS), convolutional networks that represent pixel-wise projection rays. We demonstrate the use of NRS for self-supervised learning of visual odometry and depth estimation from raw videos obtained using a wide variety of camera systems.
  arXiv  Detail & Related papers  (2020-08-15T02:29:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.