BMAN: Bidirectional Multi-Scale Aggregation Networks for Abnormal Event Detection

Lee, Sangmin; Kim, Hak Gu; Ro, Yong Man

doi:10.1109/tip.2019.2948286

Cited by 121 publications

(55 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To this end, the main approaches for abnormal event recognition involve either the use of supervised deep learning techniques to learn a dictionary of abnormal sub-events or unsupervised outlier detection techniques. in many applications [7]- [9]. Examples include surveillance in industrial environments [7] or critical infrastructures [9] for safety/security and quality assurance, traffic flow management [10] and intelligent monitoring of public places [11] Regarding outlier detection, the works of [12], [ [13], [14] learn dictionary of subevents, through a training process, and then those events that do not lie in the partitioned sub-space are marked as abnormal ones.…”

Section: Previous Workmentioning

confidence: 99%

Unsupervised Man Overboard Detection Using Thermal Imagery and Spatiotemporal Autoencoders

Bakalos¹,

Katsamenis²,

Karolou³

et al. 2021

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

Man overboard incidents in a maritime vessel are serious accidents where the rapid detection of the even is crucial for the safe retrieval of the person. To this end, the use of deep learning models as automatic detectors of these scenarios has been tested and proven efficient, however, the use of correct capturing methods is imperative in order for the learning framework to operate well. Thermal data can be a suitable method of monitoring, as they are not affected by illumination changes and are able to operate in rough conditions, such as open sea travel. We investigate the use of a convolutional autoencoder trained over thermal data, as a mechanism for the automatic detection of man overboard scenarios. Morever, we present a dataset that was created to emulate such events and was used for training and testing the algorithm.

show abstract

Section: Previous Workmentioning

confidence: 99%

Unsupervised Man Overboard Detection Using Thermal Imagery and Spatiotemporal Autoencoders

Bakalos¹,

Katsamenis²,

Karolou³

et al. 2021

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

show abstract

“…Video Anomaly Detection As most work on video anomaly detection has focused on surveillance videos, various methods have been tried to tackle this problem. Deep learning methods have been able to make significant progress toward solving this problem, using autoencoders [8,9], generative models [10], or prediction [11]. More recent methods have been able to outperform using pretraining and self-supervised learning [12,6,7].…”

Section: Related Workmentioning

confidence: 99%

Approaches Toward Physical and General Video Anomaly Detection

Kart¹,

Cohen²

2021

Preprint

View full text Add to dashboard Cite

In recent years, many works have addressed the problem of finding never-seen-before anomalies in videos. Yet, most work has been focused on detecting anomalous frames in surveillance videos taken from security cameras. Meanwhile, the task of anomaly detection (AD) in videos exhibiting anomalous mechanical behavior, has been mostly overlooked. Anomaly detection in such videos is both of academic and practical interest, as they may enable automatic detection of malfunctions in many manufacturing, maintenance, and real-life settings. To assess the potential of the different approaches to detect such anomalies, we evaluate two simple baseline approaches: (i) Temporal-pooled image AD techniques. (ii) Density estimation of videos represented with features pretrained for video-classification.Development of such methods calls for new benchmarks to allow evaluation of different possible approaches. We introduce the Physical Anomalous Trajectory or Motion (PHANTOM) dataset 1 , which contains six different video classes. Each class consists of normal and anomalous videos. The classes differ in the presented phenomena, the normal class variability, and the kind of anomalies in the videos. We also suggest an even harder benchmark where anomalous activities should be spotted on highly variable scenes.

show abstract

“…In spite of the growing interest in video anomaly detection [9, 10, 14-16, 19-21, 24, 29, 31, 36-38, 40, 43, 49, 51, 57, 58, 61, 63], which generated significant advances leading to impressive performance levels [14,15,18,24,29,53,56,57,61,63,64], the task remains very challenging. The difficulty of the task stems from two interdependent aspects: (i) the reliance on context of anomalies, and (ii) the lack of abnormal training data.…”

Section: Introductionmentioning

confidence: 99%

“…In the recent literature, we identified two distinct formulations to deal with the difficulty of the video anomaly detection task. On the one hand, we have the mainstream formulation, adopted in works such as [2,[7][8][9]12,17,20,22,24,26,27,30,32,34,35,38,40,41,44,45,47,48,53,58,59,[65][66][67], treating anomaly detection as a one-class classification (or outlier detection) task. In this formulation, training videos contain only normal events, while test videos encompass both normal and abnormal events.…”

Section: Introductionmentioning

confidence: 99%

UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection

Acsintoae¹,

Florescu²,

Mare³

et al. 2021

Preprint

View full text Add to dashboard Cite

Detecting abnormal events in video is commonly framed as a one-class classification task, where training videos contain only normal events, while test videos encompass both normal and abnormal events. In this scenario, anomaly detection is an open-set problem. However, some studies assimilate anomaly detection to action recognition. This is a closed-set scenario that fails to test the capability of systems at detecting new anomaly types. To this end, we propose UBnormal, a new supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, we introduce abnormal events annotated at the pixel level at training time, for the first time enabling the use of fully-supervised learning methods for abnormal event detection. To preserve the typical open-set formulation, we make sure to include disjoint sets of anomaly types in our training and test collections of videos. To our knowledge, UBnormal is the first video anomaly detection benchmark to allow a fair headto-head comparison between one-class open-set models and supervised closed-set models, as shown in our experiments. Moreover, we provide empirical evidence showing that UBnormal can enhance the performance of a state-of-the-art anomaly detection framework on two prominent data sets, Avenue and ShanghaiTech.

show abstract

BMAN: Bidirectional Multi-Scale Aggregation Networks for Abnormal Event Detection

Cited by 121 publications

References 43 publications

Unsupervised Man Overboard Detection Using Thermal Imagery and Spatiotemporal Autoencoders

Unsupervised Man Overboard Detection Using Thermal Imagery and Spatiotemporal Autoencoders

Approaches Toward Physical and General Video Anomaly Detection

UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection

Contact Info

Product

Resources

About