Human Kinematics-inspired Skeleton-based Video Anomaly Detection

• URL: http://arxiv.org/abs/2309.15662v1
• Date: Wed, 27 Sep 2023 13:52:53 GMT
• Title: Human Kinematics-inspired Skeleton-based Video Anomaly Detection
• Authors: Jian Xiao, Tianyuan Liu, Genlin Ji
• Abstract summary: We introduce a new idea called HKVAD (Human Kinematic-inspired Video Anomaly Detection) for video anomaly detection. Our method achieves good results with minimal computational resources, validating its effectiveness and potential.
• Score: 3.261881784285304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous approaches to detecting human anomalies in videos have typically relied on implicit modeling by directly applying the model to video or skeleton data, potentially resulting in inaccurate modeling of motion information. In this paper, we conduct an exploratory study and introduce a new idea called HKVAD (Human Kinematic-inspired Video Anomaly Detection) for video anomaly detection, which involves the explicit use of human kinematic features to detect anomalies. To validate the effectiveness and potential of this perspective, we propose a pilot method that leverages the kinematic features of the skeleton pose, with a specific focus on the walking stride, skeleton displacement at feet level, and neck level. Following this, the method employs a normalizing flow model to estimate density and detect anomalies based on the estimated density. Based on the number of kinematic features used, we have devised three straightforward variant methods and conducted experiments on two highly challenging public datasets, ShanghaiTech and UBnormal. Our method achieves good results with minimal computational resources, validating its effectiveness and potential.

Related papers

• Occlusion-Aware 3D Motion Interpretation for Abnormal Behavior Detection [10.782354892545651]
  We present OAD2D, which discriminates against motion abnormalities based on reconstructing 3D coordinates of mesh vertices and human joints from monocular videos. We reformulate the abnormal posture estimation by coupling it with Motion to Text (M2T) model in which, the VQVAE is employed to quantize motion features. Our approach demonstrates the robustness of abnormal behavior detection against severe and self-occlusions, as it reconstructs human motion trajectories in global coordinates.
  arXiv  Detail & Related papers  (2024-07-23T18:41:16Z)
• 3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data [4.130944152992895]
  We propose a novel biomechanics-aware network that directly outputs 3D kinematics from two input views. Our experiments demonstrate that the proposed approach, only trained on synthetic data, outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-20T17:33:40Z)
• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• Future Video Prediction from a Single Frame for Video Anomaly Detection [0.38073142980732994]
  Video anomaly detection (VAD) is an important but challenging task in computer vision. We introduce the task of future frame prediction proxy-task, as a novel proxy-task for video anomaly detection. This proxy-task alleviates the challenges of previous methods in learning longer motion patterns.
  arXiv  Detail & Related papers  (2023-08-15T14:04:50Z)
• Understanding the Challenges and Opportunities of Pose-based Anomaly Detection [2.924868086534434]
  Pose-based anomaly detection is a video-analysis technique for detecting anomalous events or behaviors by examining human pose extracted from the video frames. In this work, we analyze and quantify the characteristics of two well-known video anomaly datasets to better understand the difficulties of pose-based anomaly detection. We believe these experiments are beneficial for a better comprehension of pose-based anomaly detection and the datasets currently available.
  arXiv  Detail & Related papers  (2023-03-09T18:09:45Z)
• Stereo Neural Vernier Caliper [57.187088191829886]
  We propose a new object-centric framework for learning-based stereo 3D object detection. We tackle a problem of how to predict a refined update given an initial 3D cuboid guess. Our approach achieves state-of-the-art performance on the KITTI benchmark.
  arXiv  Detail & Related papers  (2022-03-21T14:36:07Z)
• Object-centric and memory-guided normality reconstruction for video anomaly detection [56.64792194894702]
  This paper addresses anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, the problem is viewed as a normality modeling strategy. Our model learns object-centric normal patterns without seeing anomalous samples during training.
  arXiv  Detail & Related papers  (2022-03-07T19:28:39Z)
• Learning Local Recurrent Models for Human Mesh Recovery [50.85467243778406]
  We present a new method for video mesh recovery that divides the human mesh into several local parts following the standard skeletal model. We then model the dynamics of each local part with separate recurrent models, with each model conditioned appropriately based on the known kinematic structure of the human body. This results in a structure-informed local recurrent learning architecture that can be trained in an end-to-end fashion with available annotations.
  arXiv  Detail & Related papers  (2021-07-27T14:30:33Z)
• Anomalous entities detection using a cascade of deep learning models [2.9005223064604078]
  This paper presents a new approach to detect anomalous entities in complex situations of examination halls. The proposed method uses a cascade of deep convolutional neural network models. Our results show that the proposed method can detect anomalous entities and warrant unusual behavior with high accuracy.
  arXiv  Detail & Related papers  (2021-03-09T01:23:19Z)
• Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior [63.11478060678794]
  We propose an effective motion-excited sampler to obtain motion-aware noise prior. By using the sparked prior in gradient estimation, we can successfully attack a variety of video classification models with fewer number of queries.
  arXiv  Detail & Related papers  (2020-03-17T10:54:12Z)
• Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection [114.9714355807607]
  We show that applying self-trained deep ordinal regression to video anomaly detection overcomes two key limitations of existing methods. We devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data.
  arXiv  Detail & Related papers  (2020-03-15T08:44:55Z)

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.