3D Convolution Neural Network based Person Identification using Gait cycles

• URL: http://arxiv.org/abs/2106.03136v1
• Date: Sun, 6 Jun 2021 14:27:06 GMT
• Title: 3D Convolution Neural Network based Person Identification using Gait cycles
• Authors: Ravi Shekhar Tiwari, Supraja P, Rijo Jackson Tom
• Abstract summary: In this work, gait features are used to identify an individual. The steps involve object detection, background subtraction, silhouettes extraction, skeletonization, and training 3D Convolution Neural Network on these gait features. The proposed method focuses more on the lower body part to extract features such as the angle between knee and thighs, hip angle, angle of contact, and many other features.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Human identification plays a prominent role in terms of security. In modern times security is becoming the key term for an individual or a country, especially for countries which are facing internal or external threats. Gait analysis is interpreted as the systematic study of the locomotive in humans. It can be used to extract the exact walking features of individuals. Walking features depends on biological as well as the physical feature of the object; hence, it is unique to every individual. In this work, gait features are used to identify an individual. The steps involve object detection, background subtraction, silhouettes extraction, skeletonization, and training 3D Convolution Neural Network on these gait features. The model is trained and evaluated on the dataset acquired by CASIA B Gait, which consists of 15000 videos of 124 subjects walking pattern captured from 11 different angles carrying objects such as bag and coat. The proposed method focuses more on the lower body part to extract features such as the angle between knee and thighs, hip angle, angle of contact, and many other features. The experimental results are compared with amongst accuracies of silhouettes as datasets for training and skeletonized image as training data. The results show that extracting the information from skeletonized data yields improved accuracy.

Related papers

• Evaluating Multiview Object Consistency in Humans and Image Models [68.36073530804296]
  We leverage an experimental design from the cognitive sciences which requires zero-shot visual inferences about object shape. We collect 35K trials of behavioral data from over 500 participants. We then evaluate the performance of common vision models.
  arXiv  Detail & Related papers  (2024-09-09T17:59:13Z)
• AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation [55.179287851188036]
  We introduce a novel all-in-one-stage framework, AiOS, for expressive human pose and shape recovery without an additional human detection step. We first employ a human token to probe a human location in the image and encode global features for each instance. Then, we introduce a joint-related token to probe the human joint in the image and encoder a fine-grained local feature.
  arXiv  Detail & Related papers  (2024-03-26T17:59:23Z)
• GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning [4.831663144935878]
  We propose DenseGait, the largest dataset for pretraining gait analysis systems containing 217K anonymized tracklets. We also propose GaitFormer, a transformer-based model that achieves 92.5% accuracy on CASIA-B and 85.33% on FVG.
  arXiv  Detail & Related papers  (2023-10-30T10:28:44Z)
• Gait Recognition Using 3-D Human Body Shape Inference [22.385670309906352]
  We present the usage of inferring 3-D body shapes distilled from limited images. We provide a method for learning 3-D body inference from silhouettes by transferring knowledge from 3-D shape prior to RGB photos.
  arXiv  Detail & Related papers  (2022-12-18T09:27:00Z)
• Body Part-Based Representation Learning for Occluded Person Re-Identification [102.27216744301356]
  Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. Part-based methods have been shown beneficial as they offer fine-grained information and are well suited to represent partially visible human bodies. We propose BPBreID, a body part-based ReID model for solving the above issues.
  arXiv  Detail & Related papers  (2022-11-07T16:48:41Z)
• Towards a Deeper Understanding of Skeleton-based Gait Recognition [4.812321790984493]
  In recent years, most gait recognition methods used the person's silhouette to extract the gait features. Model-based methods do not suffer from these problems and are able to represent the temporal motion of body joints. In this work, we propose an approach based on Graph Convolutional Networks (GCNs) that combines higher-order inputs, and residual networks.
  arXiv  Detail & Related papers  (2022-04-16T18:23:37Z)
• Privacy-Preserving Eye-tracking Using Deep Learning [1.5484595752241124]
  In this study, we focus on the case of a deep network model trained on images of individual faces. In this study, it is showed that the named model preserves the integrity of training data with reasonable confidence.
  arXiv  Detail & Related papers  (2021-06-17T15:58:01Z)
• Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking [98.91894395941766]
  We propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame. Specifically, we derive this prediction of dynamics through a graph neural network(GNN) that explicitly accounts for both spatial-temporal and visual information. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed method achieves results superior to the state of the art on both human pose estimation and tracking tasks.
  arXiv  Detail & Related papers  (2021-06-07T16:36:50Z)
• Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis [58.05389586712485]
  We tackle human image synthesis, including human motion imitation, appearance transfer, and novel view synthesis. In this paper, we propose a 3D body mesh recovery module to disentangle the pose and shape. We also build a new dataset, namely iPER dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis.
  arXiv  Detail & Related papers  (2020-11-18T02:57:47Z)
• Hidden Footprints: Learning Contextual Walkability from 3D Human Trails [70.01257397390361]
  Current datasets only tell you where people are, not where they could be. We first augment the set of valid, labeled walkable regions by propagating person observations between images, utilizing 3D information to create what we call hidden footprints. We devise a training strategy designed for such sparse labels, combining a class-balanced classification loss with a contextual adversarial loss.
  arXiv  Detail & Related papers  (2020-08-19T23:19:08Z)
• Learning landmark guided embeddings for animal re-identification [15.356786390476591]
  We propose to improve embedding learning by exploiting body landmarks information explicitly. Body landmarks are provided to the input of a CNN as confidence heatmaps. We evaluate the proposed method on a large synthetic dataset and a small real dataset.
  arXiv  Detail & Related papers  (2020-01-09T01:31:00Z)

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.