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Learning object motion patterns for anomaly detection and improved object detection

Arslan Basharat, Alexei Gritai, Mubarak Shah
2008 2008 IEEE Conference on Computer Vision and Pattern Recognition  
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]

Mohammad Baradaran, Robert Bergevin
2022 arXiv   pre-print
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

Yuan Yuan, Dong Wang, Qi Wang
2017 IEEE transactions on intelligent transportation systems (Print)  
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

Yingying Zhu, Nandita M. Nayak, Amit K. Roy-Chowdhury
2013 IEEE Journal on Selected Topics in Signal Processing  
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]

Hongyong Wang, Xinjian Zhang, Su Yang, Weishan Zhang
2021 arXiv   pre-print
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

Yan Xu, Xi Ouyang, Yu Cheng, Shining Yu, Lin Xiong, Choon-Ching Ng, Sugiri Pranata, Shengmei Shen, Junliang Xing
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
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]

Mohammad Baradaran, Robert Bergevin
2021 arXiv   pre-print
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]

Khalil Bergaoui, Yassine Naji, Aleksandr Setkov, Angélique Loesch, Michèle Gouiffès, Romaric Audigier
2022 arXiv   pre-print
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

Rensso Mora Colque, Carlos Caetano, Victor C. de Melo, Guillermo Camara Chavez, William Robson Schwartz
2018 Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications  
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

Weiming Hu, Xuejuan Xiao, Zhouyu Fu, D. Xie, Tieniu Tan, S. Maybank
2006 IEEE Transactions on Pattern Analysis and Machine Intelligence  
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  ... 
doi:10.1109/tpami.2006.176 pmid:16929731 fatcat:mg7b35qxtzenrnlmq273fkzd2y

مراجعة حول اکتشاف الأحداث الشاذة والتعرف علیها

منار دنیا, وسام البهیدى, علیاء یوسف
2021 النشرة المعلوماتیة فی الحاسبات والمعلومات  
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

Tong Lu, Liang Wu, Xiaolin Ma, Palaiahnakote Shivakumara, Chew Lim Tan
2014 2014 22nd International Conference on Pattern Recognition  
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

I. Saleemi, K. Shafique, M. Shah
2009 IEEE Transactions on Pattern Analysis and Machine Intelligence  
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

Fei Dong, Yu Zhang, Xiushan Nie
2020 IEEE Access  
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

Yachuang Feng, Yuan Yuan, Xiaoqiang Lu
2016 Proceedings of the 2016 ACM on Multimedia Conference - MM '16  
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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