1,784 research outputs found
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models
Video Anomaly Detection (VAD) serves as a pivotal technology in the
intelligent surveillance systems, enabling the temporal or spatial
identification of anomalous events within videos. While existing reviews
predominantly concentrate on conventional unsupervised methods, they often
overlook the emergence of weakly-supervised and fully-unsupervised approaches.
To address this gap, this survey extends the conventional scope of VAD beyond
unsupervised methods, encompassing a broader spectrum termed Generalized Video
Anomaly Event Detection (GVAED). By skillfully incorporating recent
advancements rooted in diverse assumptions and learning frameworks, this survey
introduces an intuitive taxonomy that seamlessly navigates through
unsupervised, weakly-supervised, supervised and fully-unsupervised VAD
methodologies, elucidating the distinctions and interconnections within these
research trajectories. In addition, this survey facilitates prospective
researchers by assembling a compilation of research resources, including public
datasets, available codebases, programming tools, and pertinent literature.
Furthermore, this survey quantitatively assesses model performance, delves into
research challenges and directions, and outlines potential avenues for future
exploration.Comment: Accepted by ACM Computing Surveys. For more information, please see
our project page: https://github.com/fudanyliu/GVAE
Survey on video anomaly detection in dynamic scenes with moving cameras
The increasing popularity of compact and inexpensive cameras, e.g.~dash
cameras, body cameras, and cameras equipped on robots, has sparked a growing
interest in detecting anomalies within dynamic scenes recorded by moving
cameras. However, existing reviews primarily concentrate on Video Anomaly
Detection (VAD) methods assuming static cameras. The VAD literature with moving
cameras remains fragmented, lacking comprehensive reviews to date. To address
this gap, we endeavor to present the first comprehensive survey on Moving
Camera Video Anomaly Detection (MC-VAD). We delve into the research papers
related to MC-VAD, critically assessing their limitations and highlighting
associated challenges. Our exploration encompasses three application domains:
security, urban transportation, and marine environments, which in turn cover
six specific tasks. We compile an extensive list of 25 publicly-available
datasets spanning four distinct environments: underwater, water surface,
ground, and aerial. We summarize the types of anomalies these datasets
correspond to or contain, and present five main categories of approaches for
detecting such anomalies. Lastly, we identify future research directions and
discuss novel contributions that could advance the field of MC-VAD. With this
survey, we aim to offer a valuable reference for researchers and practitioners
striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie
Abnormal Event Detection Using HOSF
[[abstract]]In this paper a simple and effective crowd behavior normality method is proposed. We use the histogram of oriented social force (HOSF) as the feature vector to encode the observed events of a surveillance video. A dictionary of codewords is trained to include typical HOSFs. To detect whether an event is normal is accomplished by comparing how similar to the closest codeword via z-value. The proposed method includes the following characteristic: (1) the training is automatic without human labeling; (2) instead of object tracking, the method integrates particles and social force as feature descriptors; (3) z-score is used in measuring the normality of events. The method is testified by the UMN dataset with promising results.[[conferencetype]]國際[[conferencedate]]20131216~20131218[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Maca
Video trajectory analysis using unsupervised clustering and multi-criteria ranking
Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead
A Real-Time Implementation of Moving Object Action Recognition System Based on Motion Analysis
This paper proposes a PixelStreams-based FPGA implementation of a real-time system that can detect and recognize human activity using Handel-C. In the first part of our work, we propose a GUI programmed using Visual C++ to facilitate the implementation for novice users. Using this GUI, the user can program/erase the FPGA or change the parameters of different algorithms and filters. The second part of this work details the hardware implementation of a real-time video surveillance system on an FPGA, including all the stages, i.e., capture, processing, and display, using DK IDE. The targeted circuit is an XC2V1000 FPGA embedded on Agility’s RC200E board. The PixelStreams-based implementation was successfully realized and validated for real-time motion detection and recognition
Demand pattern analysis of taxi trip data for anomalies detection and explanation
Novi Zakon o obveznim odnosima promijenio je naziv instituta bankarske garancije u bankarsko jamstvo i pojam tog instituta izložen u čl. 1039. st. 1. i 2. (tako da se sada pod nazivom bankarskog jamstva pojavljuje samostalna bankarska garancija), dok ostale odredbe ranijeg ZOO-a sadržajno nisu promijenjene. Bankovna garancija jeste samostalna obveza banke garanta koja je akcesorna obveza jamca. Banka garant ne osigurava ispunjenje obveze glavnog dužnika, naprotiv, obvezuje se korisniku garancije nadoknaditi štetu, odnosno izvršiti obvezu koju u ugovorenom roku nije izvršio glavni dužnik. U radu izlažem pitanja u svezi s oblikom i vrstama garancije, kvalifikacijom i nastankom bančine obveze prema korisniku, pretpostavkama isplate, prenosivošću i potvrdom garancije kao i njihovoj zlouporabi.The new Law of mandatory relations has changed the name of the bank warranty to bank assurance and complete connotation is represented in article 1039. in section 1 and 2. (according to which, under the name of bank warranty is independent bank assurance) while other provisions from the Law of mandatory relations have not been significantly contextually changed. Bank warranty is independent obligation of the warrant bank, which is accessory obligation of the guarantor. Warrant bank does not assure implementation of the main debtor’s obligation, but it commits to compensate potential detriment towards the warranty user, in the other words, implement the obligation which has not been realized by the main debtor in specified time period
A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation
Semi-supervised video anomaly detection (VAD) is a critical task in the
intelligent surveillance system. However, an essential type of anomaly in VAD
named scene-dependent anomaly has not received the attention of researchers.
Moreover, there is no research investigating anomaly anticipation, a more
significant task for preventing the occurrence of anomalous events. To this
end, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes,
28 classes of abnormal events, and 16 hours of videos. At present, it is the
largest semi-supervised VAD dataset with the largest number of scenes and
classes of anomalies, the longest duration, and the only one considering the
scene-dependent anomaly. Meanwhile, it is also the first dataset proposed for
video anomaly anticipation. We further propose a novel model capable of
detecting and anticipating anomalous events simultaneously. Compared with 7
outstanding VAD algorithms in recent years, our method can cope with
scene-dependent anomaly detection and anomaly anticipation both well, achieving
state-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and
the newly proposed NWPU Campus datasets consistently. Our dataset and code is
available at: https://campusvad.github.io.Comment: CVPR 202
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