Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-directional LSTM

• URL: http://arxiv.org/abs/2007.07773v2
• Date: Mon, 17 Aug 2020 05:46:24 GMT
• Title: Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-directional LSTM
• Authors: Yong Chen, Lu Wang, Jiajia Hu, Mingbin Ye
• Abstract summary: Mask R-CNN method based on deep learning technique can clearly extract the moving object in noise background. We propose an attention guided Bi-directional LSTM model for the final fall event detection.
• Score: 7.103237947935741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fall event detection, as one of the greatest risks to the elderly, has been a hot research issue in the solitary scene in recent years. Nevertheless, there are few researches on the fall event detection in complex background. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method based on deep learning technique can clearly extract the moving object in noise background. We further propose an attention guided Bi-directional LSTM model for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation.

Related papers

• RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection [4.231702796492545]
  This study introduces a Robust Anomaly Detection dataset with free views, uneven illuminations, and blurry collections. RAD aims to identify foreign objects on working platforms as anomalies. We assess and analyze 11 state-of-the-art unsupervised and zero-shot methods on RAD.
  arXiv  Detail & Related papers  (2024-06-11T11:39:44Z)
• Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection [101.15777242546649]
  Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection. We present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge.
  arXiv  Detail & Related papers  (2024-06-01T17:32:26Z)
• CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection [1.951082473090397]
  We propose a self-supervised anomaly detection approach that combines contrastive learning with 2D-Flow. Compared to mainstream unsupervised approaches, our self-supervised method demonstrates superior detection accuracy, fewer additional model parameters, and faster inference speed. Our approach showcases new state-of-the-art results, achieving a performance of 99.6% in image-level AUROC on the MVTecAD dataset and 96.8% in image-level AUROC on the BTAD dataset.
  arXiv  Detail & Related papers  (2023-11-12T10:07:03Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Spatio-Temporal Point Process for Multiple Object Tracking [30.041104276095624]
  Multiple Object Tracking (MOT) focuses on modeling the relationship of detected objects among consecutive frames and merge them into different trajectories. We propose a novel framework that can effectively predict and mask-out noisy and confusing detection results before associating objects into trajectories.
  arXiv  Detail & Related papers  (2023-02-05T18:14:08Z)
• Spatio-Temporal Context Modeling for Road Obstacle Detection [12.464149169670735]
  A data-driven context-temporal model of the driving scene is constructed with the layouts of the training data. Obstacles are detected via state-of-the-art object detection algorithms, and the results are combined with the generated scene.
  arXiv  Detail & Related papers  (2023-01-19T07:06:35Z)
• Object Class Aware Video Anomaly Detection through Image Translation [1.2944868613449219]
  This paper proposes a novel two-stream object-aware VAD method that learns the normal appearance and motion patterns through image translation tasks. The results show that, as significant improvements to previous methods, detections by our method are completely explainable and anomalies are localized accurately in the frames.
  arXiv  Detail & Related papers  (2022-05-03T18:04:27Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)
• Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery [133.83048723991462]
  We propose a novel ensemble and random collaborative representation-based detector (ERCRD) for hyperspectral anomaly detection (HAD) Our experiments on four real hyperspectral datasets exhibit the accuracy and efficiency of this proposed ERCRD method compared with ten state-of-the-art HAD methods.
  arXiv  Detail & Related papers  (2021-01-06T11:23:51Z)
• Solving Missing-Annotation Object Detection with Background Recalibration Loss [49.42997894751021]
  This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets. Previous art has proposed to use soft sampling to re-weight the gradients of RoIs based on the overlaps with positive instances, while their method is mainly based on the two-stage detector. In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image.
  arXiv  Detail & Related papers  (2020-02-12T23:11:46Z)

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.