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Learning object motion patterns for anomaly detection and improved object detection
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
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We present a novel framework for learning patterns of motion and sizes of objects in static camera surveillance. ...
We also show the use of this scene model to improve object detection through pixel-level parameter feedback of the minimum object size and background learning rate. ...
This knowledge is used to build a scene model which can be used to detect abnormal motion patterns and to enhance the surveillance performance by improving object detection. ...
doi:10.1109/cvpr.2008.4587510
dblp:conf/cvpr/BasharatGS08
fatcat:ybx64mqtlnh4viqtrkjuyfhyhe
Object Class Aware Video Anomaly Detection through Image Translation
[article]
2022
arXiv
pre-print
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Semi-supervised video anomaly detection (VAD) methods formulate the task of anomaly detection as detection of deviations from the learned normal patterns. ...
To tackle these challenges, this paper proposes a novel two-stream object-aware VAD method that learns the normal appearance and motion patterns through image translation tasks. ...
Deep learning (DL) based video anomaly detection methods have achieved significant improvements with respect to their classic counterparts. ...
arXiv:2205.01706v1
fatcat:cyw3kgcp6rcznpn2qvf3jlhjv4
Anomaly Detection in Traffic Scenes via Spatial-Aware Motion Reconstruction
2017
IEEE transactions on intelligent transportation systems (Print)
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2019 pre-print arXiv:1904.13079v1 fatcat:p26ainctsbctzfj47iftoxoiyy
To tackle these specific problems, this paper proposes a spatial localization constrained sparse coding approach for anomaly detection in traffic scenes, which firstly measures the abnormality of motion ...
The main contributions are threefold: 1) This work describes the motion orientation and magnitude of the object respectively in a new way, which is demonstrated to be better than the traditional motion ...
From Table III , there is a significantly improvement after incorporation. As for incorporating strategy, we just add the object detection score on anomaly map and re-normalize it into range [0, 1]. ...
doi:10.1109/tits.2016.2601655
fatcat:ep3osyr3zjgllnfu5j6fi34scq
Context-Aware Activity Recognition and Anomaly Detection in Video
2013
IEEE Journal on Selected Topics in Signal Processing
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The learned model and generated labels are used to detect anomalies whose motion and context patterns deviate from the learned patterns. ...
In this paper, we propose a mathematical framework to jointly model related activities with both motion and context information for activity recognition and anomaly detection. ...
Activities whose patterns deviate from the learned frequent patterns are detected as anomalies. ...
doi:10.1109/jstsp.2012.2234722
fatcat:rw2ct5k55fgczo4zjfe5eybnmm
Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow
[article]
2021
arXiv
pre-print
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Meanwhile, We extend the appearance-motion correspondence scheme from frame reconstruction to prediction, which not only helps to learn the knowledge about object appearances and correlated motion, but ...
Video anomaly detection is a challenging task because of diverse abnormal events. ...
Due to that our model has never learned the patterns of the abnormal object, the car, our model has mistaken its motion trend, by which this anomaly will be distinguished. ...
arXiv:2105.04302v1
fatcat:pjc3i5tz5nfnjdjbsgjmu5p4ra
Dual-Mode Vehicle Motion Pattern Learning for High Performance Road Traffic Anomaly Detection
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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In this work, we present a model to detect anomaly in road traffic by learning from the vehicle motion patterns in two distinctive yet correlated modes, i.e., the static mode and the dynamic mode, of the ...
The dynamic mode analysis of the vehicles is learned from detected and tracked vehicle trajectories to find the abnormal trajectory which is aberrant from the dominant motion patterns. ...
Facing with the above issues, we propose a dual-mode vehicle motion pattern learning model for anomaly detection in road traffic, which performs joint analyses of both the static and moving vehicles. ...
doi:10.1109/cvprw.2018.00027
dblp:conf/cvpr/XuOCYXNPSX18
fatcat:x73kkxrmsfaphkjvpkwt6wqtlm
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods
[article]
2021
arXiv
pre-print
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for anomaly detection. ...
This paper introduces the researchers of the field to a new perspective and reviews the recent deep-learning based semi-supervised video anomaly detection approaches, based on a common strategy they use ...
objects and improves the performance. ...
arXiv:2111.01604v1
fatcat:jvgatw3khnh2np237sw2loitde
Object-centric and memory-guided normality reconstruction for video anomaly detection
[article]
2022
arXiv
pre-print
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Our framework leverages both appearance and motion information to learn object-level behavior and captures prototypical patterns within a memory module. ...
This paper addresses video anomaly detection problem for videosurveillance. ...
We can see that both appearance and motion features are necessary to model usual actions to better detect anomalies. ...
arXiv:2203.03677v1
fatcat:p5qu75up6vcetjjn2xemns744y
Novel Anomalous Event Detection based on Human-object Interactions
2018
Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
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at the same location or in the same scene for the learning and test stages of anomaly event detection), making our approach able to learn normal patterns (i.e., patterns that do not entail an anomaly) ...
Our paradigm shift anomalous event detection approach exploits human-object interactions to learn normal behavior patterns from a specific context. ...
Research Foundation -FAPEMIG (Grants APQ-00567-14 and PPM-00540-17) and the Coordination for the Improvement of Higher Education Personnel -CAPES (DeepEyes Project). ...
doi:10.5220/0006615202930300
dblp:conf/visapp/ColqueCMCS18
fatcat:g4727jvfoballmrqivkms3o6ka
A system for learning statistical motion patterns
2006
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. For the most part, objects in the scene do not move randomly. ...
In this paper, we present a system for learning object motion patterns which are then used to detect anomalies and predict behaviors. Our system is original in the following ways: . ...
For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib. ...
doi:10.1109/tpami.2006.176
pmid:16929731
fatcat:mg7b35qxtzenrnlmq273fkzd2y
مراجعة حول اکتشاف الأحداث الشاذة والتعرف علیها
2021
النشرة المعلوماتیة فی الحاسبات والمعلومات
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This paper presents a survey on both handcrafted and deep learning models for abnormal events detection. ...
Such detection requires detecting and tracking objects then recognize what is happening around those tracked objects. ...
II) Motion anomaly refers
to an unusual motion of normal appearance object. ...
doi:10.21608/fcihib.2020.47617.1004
fatcat:huqdqx64cfgzlh3jkzmgsoqjaq
Anomaly Detection through Spatio-temporal Context Modeling in Crowded Scenes
2014
2014 22nd International Conference on Pattern Recognition
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The proposed framework essentially turns the anomaly detection process into two parts, namely, motion pattern representation and crowded context modeling. ...
A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented in this paper. ...
In [5] , an anomaly detection system is proposed to automatically learn motion patterns by tracking multiple objects, in which growing and prediction of cluster centroids of foreground pixels ensure the ...
doi:10.1109/icpr.2014.383
dblp:conf/icpr/LuWMST14
fatcat:hxhlijvm6fhyfo5des3qoceaka
Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance
2009
IEEE Transactions on Pattern Analysis and Machine Intelligence
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during tracking, and deciding whether a given trajectory represents an anomaly to the observed motion patterns. ...
Once the model is learned, we use a unified Markov Chain Monte Carlo (MCMC)-based framework for generating the most likely paths in the scene, improving foreground detection, persistent labeling of objects ...
Yaser Sheikh, and Dr. Marshall Tappen for their valuable comments throughout this research. ...
doi:10.1109/tpami.2008.175
pmid:19542580
fatcat:6v2pwcd3nnevxkywk4h5ldnisq
Dual Discriminator Generative Adversarial Network for Video Anomaly Detection
2020
IEEE Access
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Because of the rarity of abnormal events and the complicated characteristic of videos, video anomaly detection is challenging and has been studied for a long time. ...
Video anomaly detection is an essential task because of its numerous applications in various areas. ...
The goal of semisupervised anomaly detection is to learn a model or a representation that captures normal motion and spatial appearance patterns [3] . ...
doi:10.1109/access.2020.2993373
fatcat:y7qrrnapcnbp3agngpwkr5csp4
Deep Representation for Abnormal Event Detection in Crowded Scenes
2016
Proceedings of the 2016 ACM on Multimedia Conference - MM '16
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Specially, appearance, texture, and short-term motion features are automatically learned and fused with stacked denoising autoencoders. ...
Experiments and comparisons on real world datasets show that the proposed algorithm outperforms state of the arts for the abnormal event detection problem in crowded scenes. ...
[4] compute feature responses for surrounding annular windows, and detect anomalies as center-surround salient objects. ...
doi:10.1145/2964284.2967290
dblp:conf/mm/FengYL16
fatcat:2bbpelq57natnettwpyucogl6e
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