Anomalous entities detection using a cascade of deep learning models
- URL: http://arxiv.org/abs/2103.05164v1
- Date: Tue, 9 Mar 2021 01:23:19 GMT
- Title: Anomalous entities detection using a cascade of deep learning models
- Authors: Hamza Riaz, Muhammad Uzair and Habib Ullah
- Abstract summary: 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.
- Score: 2.9005223064604078
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Human actions that do not conform to usual behavior are considered as
anomalous and such actors are called anomalous entities. Detection of anomalous
entities using visual data is a challenging problem in computer vision. 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. In the first stage, we apply a pretrained
model of human pose estimation on frames of videos to extract key feature
points of body. Patches extracted from each key point are utilized in the
second stage to build a densely connected deep convolutional neural network
model for detecting anomalous entities. For experiments we collect a video
database of students undertaking examination in a hall. Our results show that
the proposed method can detect anomalous entities and warrant unusual behavior
with high accuracy.
Related papers
- Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories [0.5774786149181392]
We develop a lightweight anomaly detection model specifically designed to detect anomalies in human trajectories.
Our algorithm consists of two main modules. The first is the collaborative filtering module, which applies collaborative filtering to model normal mobility of individual humans to places of interest.
To validate our approach, we conducted extensive experiments using simulated and real-world datasets comparing to numerous state-of-the-art trajectory anomaly detection approaches.
arXiv Detail & Related papers (2024-09-27T03:28:11Z) - Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image.
In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting.
Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
arXiv Detail & Related papers (2023-11-27T13:14:06Z) - 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) - Human Kinematics-inspired Skeleton-based Video Anomaly Detection [3.261881784285304]
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.
arXiv Detail & Related papers (2023-09-27T13:52:53Z) - 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) - Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold.
We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples.
We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
arXiv Detail & Related papers (2022-06-23T14:16:30Z) - 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) - SLA$^2$P: Self-supervised Anomaly Detection with Adversarial
Perturbation [77.71161225100927]
Anomaly detection is a fundamental yet challenging problem in machine learning.
We propose a novel and powerful framework, dubbed as SLA$2$P, for unsupervised anomaly detection.
arXiv Detail & Related papers (2021-11-25T03:53:43Z) - Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
We introduce a novel weakly-supervised anomaly detection framework to train detection models.
The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability.
Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
arXiv Detail & Related papers (2021-08-01T14:33:17Z) - MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly Detection [16.914663209964697]
We propose our deep learning approach to the anomaly detection problem named Multi-LayerOne-Class Classification (MOCCA)
We explicitly leverage the piece-wise nature of deep neural networks by exploiting information extracted at different depths to detect abnormal data instances.
We show that our method reaches superior performances compared to the state-of-the-art approaches available in the literature.
arXiv Detail & Related papers (2020-12-09T08:32:56Z)
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.