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Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos? [article]

Tiago S. Nazare, Rodrigo F. de Mello, Moacir A. Ponti
2018 arXiv   pre-print
Recently, several techniques have been explored to detect unusual behaviour in surveillance videos.  ...  Nevertheless, few studies leverage features from pre-trained CNNs and none of then present a comparison of features generate by different models.  ...  EXPERIMENTAL SETUP In order to measure how well the features extracted from pre-trained image classification CNNs perform, when detecting anomalies in surveillance videos, we employed the experimental  ... 
arXiv:1811.08495v1 fatcat:no7g3zrnx5bopfyvrwlckquese

Automatic Detection of Violent Incidents from Video Footage of CCTV Cameras

Baswaraju Swathi, B L Deepika Chowdary, K Sai Sindhu, Ashika P
2020 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
In the current era, the majority of public places such as supermarket, public garden, malls, university campus, etc. are under video surveillance.  ...  Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. We present an e?cient method for detecting anomalies in videos.  ...  By incorporating convolutional feature extractor in both spatial and temporal space into the encoding-decoding structure, we build an end-to-end trainable model for video anomaly detection.  ... 
doi:10.32628/cseit206355 fatcat:dlmqvhzeezeqxh3i6rgpse7ue4

Deep anomaly detection through visual attention in surveillance videos

Nasaruddin Nasaruddin, Kahlil Muchtar, Afdhal Afdhal, Alvin Prayuda Juniarta Dwiyantoro
2020 Journal of Big Data  
This paper describes a method for learning anomaly behavior in the video by finding an attention region from spatiotemporal information, in contrast to the full-frame learning.  ...  In total, there are approximately ~ 13 million frames are learned during the training and testing phase.  ...  As illustrated in Fig. 6 , for its good performance and efficiency, the popular Convolutional 3D Networks (C3D) [43] is selected as our pre-trained feature extractor.  ... 
doi:10.1186/s40537-020-00365-y fatcat:cn2dsuzxlrg7db3qyqlxqihvia

A Modular and Unified Framework for Detecting and Localizing Video Anomalies [article]

Keval Doshi, Yasin Yilmaz
2021 arXiv   pre-print
Anomaly detection in videos has been attracting an increasing amount of attention.  ...  Furthermore, current state-of-the-art approaches are evaluated using the standard instance-based detection metric by considering video frames as independent instances, which is not ideal for video anomaly  ...  Transfer Learning-Based Feature Extraction In general, the end-to-end training of DNNs for video anomaly detection necessitates focusing on a particular aspect in which anomalies may occur, such as object  ... 
arXiv:2103.11299v1 fatcat:cqip5pyg5bfzfgiocrmrr252p4

AAD: Adaptive Anomaly Detection through traffic surveillance videos [article]

Mohammmad Farhadi Bajestani, Seyed Soroush Heidari Rahmat Abadi, Seyed Mostafa Derakhshandeh Fard, Roozbeh Khodadadeh
2018 arXiv   pre-print
location in the video scene as the first step to implement anomaly detection.  ...  Basically, we propose an alternative method for unusual activity detection using an adaptive anomaly detection framework.  ...  We are using ResNet 101 as feature extractor for FasterRCNN which has been pre-trained over ImageNet.  ... 
arXiv:1808.10044v1 fatcat:7kmxqup2n5cfxmgvbpcgscujoi

Generalization of feature embeddings transferred from different video anomaly detection domains [article]

Fernando Pereira dos Santos, Leonardo Sampaio Ferraz Ribeiro, Moacir Antonelli Ponti
2019 arXiv   pre-print
This paper investigates video anomaly detection, in particular feature embeddings of pre-trained CNN that can be used with non-fully supervised data.  ...  Detecting anomalous activity in video surveillance often involves using only normal activity data in order to learn an accurate detector.  ...  Acknowledgment The authors would like to thank FAPESP for the grants #2016/16111-4 and #2017/22366-8, and CNPq (307973/2017-4).  ... 
arXiv:1901.09819v1 fatcat:rr7rstnhzbe2nmhk5qe5zzsuqi

An On-Line and Adaptive Method for Detecting Abnormal Events in Videos Using Spatio-Temporal ConvNet

Samir Bouindour, Hichem Snoussi, Mohamad Hittawe, Nacef Tazi, Tian Wang
2019 Applied Sciences  
We address in this paper the problem of abnormal event detection in video-surveillance. In this context, we use only normal events as training samples.  ...  Experimental results on challenging datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level in anomaly detection task.  ...  Acknowledgments: The authors are grateful to anonymous reviewers for their comments that considerably enhanced the quality of the paper.  ... 
doi:10.3390/app9040757 fatcat:x6qylek4hvh2rogz3jcq2jsc2m

Modeling Representation of Videos for Anomaly Detection using Deep Learning: A Review [article]

Yong Shean Chong, Yong Haur Tay
2015 arXiv   pre-print
We address the most fundamental aspect for video anomaly detection, that is, video feature representation.  ...  In this paper, we would like to review the existing methods of modeling video representations using deep learning techniques for the task of anomaly detection and action recognition.  ...  Thus, many research efforts are done to replace the need of manually detecting anomalous situations, to create an automated video surveillance system.  ... 
arXiv:1505.00523v1 fatcat:6kaxqtedwjedbajk7abs2vuita

An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract)

U-Ju Gim, Jae-Jun Lee, Jeong-Hun Kim, Young-Ho Park, Aziz Nasridinov
In this study, we propose an automatic detection system of shoplifting behaviors from surveillance videos.  ...  Thus, there is a need for intelligent CCTV surveillance systems that ensure the integrity of shops, despite workforce shortages.  ...  We extracted a person object as an ROI using Mask-R-CNN with a pre-trained dataset.  ... 
doi:10.1609/aaai.v34i10.7169 fatcat:yr4ssvk6fbf2xapv2uzl4xa5yi

Towards Anomaly Detection in Dashcam Videos [article]

Sanjay Haresh, Sateesh Kumar, M. Zeeshan Zia, Quoc-Huy Tran
2020 arXiv   pre-print
In addition, we share insights into the behavior of these two important families of anomaly detection approaches on dashcam data.  ...  We propose to apply data-driven anomaly detection ideas from deep learning to dashcam videos, which hold the promise of bridging this gap.  ...  RetroTrucks -A NEW DATASET FOR DASHCAM ANOMALY DETECTION A. Previous Datasets Most existing datasets are aimed at understanding anomalies in surveillance videos. Li et al.  ... 
arXiv:2004.05261v2 fatcat:ccs2ubugabh6bbw2422dozks7q

Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild

Mohammad Ibrahim Sarker, Cristina Losada-Gutiérrez, Marta Marrón-Romera, David Fuentes-Jiménez, Sara Luengo-Sánchez
2021 Sensors  
In this work, it is proposed an approach for anomaly detection in video-surveillance scenes based on a weakly supervised learning algorithm.  ...  In this video-surveillance context, anomalies occur only for a very short time, and very occasionally.  ...  features are extracted for each video segment using a T-C3D [30] as a feature extractor.  ... 
doi:10.3390/s21123993 pmid:34207883 pmcid:PMC8230050 fatcat:kvxd3ujt3rfohf5qg4s3eb4hzm

IBaggedFCNet: An Ensemble Framework for Anomaly Detection in Surveillance Videos

Yumna Zahid, Muhammad A. Tahir, Muhammad N. Durrani, Ahmed Bouridane
2020 IEEE Access  
Video feature extraction plays a crucial part in anomaly detection.  ...  Pre-trained CNNs such as LeNet-5 [40] , AlexNet [41] etc. on large image datasets have allowed deep feature learning and extraction.  ... 
doi:10.1109/access.2020.3042222 fatcat:ebflecvnszfr5beyy6q5xb2kxa

A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos [article]

Xianlin Zeng, Yalong Jiang, Wenrui Ding, Hongguang Li, Yafeng Hao, Zifeng Qiu
2021 arXiv   pre-print
Deep learning models have been widely used for anomaly detection in surveillance videos.  ...  An understanding of scenes is achieved and serves anomaly detection.  ...  Although the CNN-based method has achieved great success in many visual tasks, the applications of CNN in anomaly detection are still limited by two constraints.  ... 
arXiv:2112.04294v2 fatcat:vi26nkpf2jhc7b2sjseo4b4rii

Automatic Detection of Traffic Accidents from Video Using Deep Learning Techniques

Sergio Robles-Serrano, German Sanchez-Torres, John Branch-Bedoya
2021 Computers  
Due to this and the wide use of video surveillance and intelligent traffic systems, an automated traffic accident detection approach becomes desirable for computer vision researchers.  ...  The visual and temporal features are learned in the training phase through convolution and recurrent layers using built-from-scratch and public datasets.  ...  However, this pre-trained model does not show good results when detecting a traffic accident in images because the model was trained with a completely different task.  ... 
doi:10.3390/computers10110148 fatcat:sisgyzbehrdcjp7dwwnkfbedtq

A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods [article]

Mohammad Baradaran, Robert Bergevin
2021 arXiv   pre-print
Anomalies are one of the main detection targets in surveillance systems, usually needing real-time actions.  ...  Unlike previous surveys, DNNs are reviewed from a spatiotemporal feature extraction viewpoint, customized for video anomaly detection.  ...  [16] is a good example for the application of pre-trained CNNs for extracting appearance features to detect anomalies in videos. 4: Convolutional neural networks (CNNs) are suitable for processing an  ... 
arXiv:2111.01604v1 fatcat:jvgatw3khnh2np237sw2loitde
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