8 research outputs found
Predicting Next Local Appearance for Video Anomaly Detection
We present a local anomaly detection method in videos. As opposed to most
existing methods that are computationally expensive and are not very
generalizable across different video scenes, we propose an adversarial
framework that learns the temporal local appearance variations by predicting
the appearance of a normally behaving object in the next frame of a scene by
only relying on its current and past appearances. In the presence of an
abnormally behaving object, the reconstruction error between the real and the
predicted next appearance of that object indicates the likelihood of an
anomaly. Our method is competitive with the existing state-of-the-art while
being significantly faster for both training and inference and being better at
generalizing to unseen video scenes.Comment: Accepted as an oral presentation for MVA'202
Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention
The rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then modeled as a time-series classification problem. The proposed method was tested on the popular UCF Crime dataset, and benchmarked against the current state-of-the-art robust temporal feature magnitude (RTFM) method, which relies on the Inflated 3D ConvNet (I3D) preprocessing method. Our results demonstrate an impressive 8.45-fold increase in detection inference speed compared to the state-of-the-art RTFM, along with an F1 score of 92%,outperforming RTFM by 3%. Furthermore, our method achieved these results without requiring expensive data augmentation or image feature extraction
Anomaly Detection under Distribution Shift
Anomaly detection (AD) is a crucial machine learning task that aims to learn
patterns from a set of normal training samples to identify abnormal samples in
test data. Most existing AD studies assume that the training and test data are
drawn from the same data distribution, but the test data can have large
distribution shifts arising in many real-world applications due to different
natural variations such as new lighting conditions, object poses, or background
appearances, rendering existing AD methods ineffective in such cases. In this
paper, we consider the problem of anomaly detection under distribution shift
and establish performance benchmarks on four widely-used AD and
out-of-distribution (OOD) generalization datasets. We demonstrate that simple
adaptation of state-of-the-art OOD generalization methods to AD settings fails
to work effectively due to the lack of labeled anomaly data. We further
introduce a novel robust AD approach to diverse distribution shifts by
minimizing the distribution gap between in-distribution and OOD normal samples
in both the training and inference stages in an unsupervised way. Our extensive
empirical results on the four datasets show that our approach substantially
outperforms state-of-the-art AD methods and OOD generalization methods on data
with various distribution shifts, while maintaining the detection accuracy on
in-distribution data. Code and data are available at
https://github.com/mala-lab/ADShift.Comment: Accepted at ICCV 202
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
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network
The widespread adoption of city surveillance systems has led to an increase in the use of surveillance videos for maintaining public safety and security. This thesis tackles the problem of detecting anomalous events in surveillance videos. The goal is to automatically identify abnormal events by learning from both normal and abnormal videos. Most of previous works consider any deviation from learned normal patterns as an anomaly, which may not always be valid since the same activity could be normal or abnormal under different circumstances. To address this issue, the thesis utilizes the Two-Stream Inflated 3D (I3D) Convolutional Networks to extract spatial and temporal video features and demonstrates how it outperforms the 3D Convolutional Network (C3D) used in prior work as feature extractor. To avoid annotating abnormal activities in training videos, a weakly supervised anomaly detection model is implemented based on the Multiple Instance Learning (MIL) framework. The model considers normal and abnormal videos as bags and video clips as instances, learns a ranking model to predict high anomaly scores for video clips containing anomalies. The thesis further shows that the choice of features input, such as concatenating RGB and flow features, and careful choice of optimization settings, such as optimizer, can significantly improve the performance of the anomaly detection model on some evaluation metrics
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Deep Anomaly Detection and Distribution Shifts
Anomaly detection is important in various applications, from cyber-security, transportation, industry, and finance to healthcare. The anomaly detection problem is to identify anomalies originating from a different data-generating process from normal data. The rare occurrence of anomalies and their unknown causes makes it hard to collect and model them. Thus, anomaly detection methods utilize normal data to build anomaly detectors. In this dissertation, we apply deep anomaly detection methods--methods that apply deep learning techniques--to solve anomaly detection problems. We contribute multiple generic frameworks for various anomaly detection setups. First, we challenge the common clean training data assumption (free of anomalies) and stress that practical training data is often contaminated with unnoticed anomalies. We propose a novel unsupervised training strategy for training an anomaly detector in the presence of unlabeled anomalies that is compatible with a broad class of models. Second, selecting informative data points for expert feedback can significantly improve anomaly detection performance. The critical challenges are selecting the most informative samples for expert review and effectively incorporating their feedback to bolster anomaly detection capabilities. To address these challenges, we propose a new data labeling strategy and a new learning framework for active and semi-supervised anomaly detection. Third, real-world applications may face distribution shifts. We consider the online learning problem where the shifts occur at unknown positions and with unknown intensities. We derive a new Bayesian online inference approach to automatically infer these distribution shifts and adapt the model to the detected changes. This approach applies to both supervised and unsupervised learning settings. We also consider the problem of adapting an anomaly detector to drift in the normal data distribution, especially when no training data is available for the “new normal.” This setting is called zero-shot anomaly detection. We propose a simple yet effective method that combines batch normalization and meta-training for zero-shot anomaly detection. The learning frameworks introduced in this dissertation are model-agnostic and apply to various data types. Extensive experiments demonstrate the efficacy of our proposed approaches