Weakly Supervised Video Anomaly Detection via Center-Guided Discriminative Learning

In surveillance environments, detecting anomalies requires understanding the contextual dynamics of the environment, human behaviors, and movements within a scene. Effective anomaly detection must address both the where and what of events, but existing approaches such as unimodal action-based methods or LLM-integrated multimodal frameworks have limitations. These methods either rely on implicit scene information, making it difficult to localize where anomalies occur, or fail to adapt to surveillance specific challenges such as view changes, subtle actions, low light conditions, and crowded scenes. As a result, these challenges hinder accurate detection of what occurs. To overcome these limitations, our system takes advantage of features from a lightweight scene classification model to discern where an event occurs, acquiring explicit location-based context. To identify what events occur, it focuses on atomic actions, which remain underexplored in this field and are better suited to interpreting intricate abnormal behaviors than conventional abstract action features. To achieve robust anomaly detection, the proposed Temporal-Semantic Relationship Network (TSRN) models spatio-temporal relationships among multimodal features and employs a Segment-selective Focal Margin loss (SFML) to effectively address class imbalance, outperforming conventional MIL-based methods. Experimental results on public datasets demonstrate that the proposed system effectively reduces false alarms while maintaining robustness and practicality for real-world surveillance applications.

For enhancing public safety, a surveillance system is essential. Specifically, video surveillance is the most popular way to maintain safety in public and private areas. The detection and recognition of abnormal activity is difficult due to a complex environment, video quality, and varying noise levels. Addressing the challenges of accuracy and video processing, the proposed study uses a cross-attention network with feature fusion to improve the recognition of abnormal activity in complex scenarios. Cross-attention helps to capture contextual information from different videos. The proposed model combines an innovative method of cross attention and feed-forward attention with latent space representation-based fusion, aiming to improve accuracy. The simulation of the study uses two benchmark datasets, UCF and UCSD and achieves remarkable performance with 97.1 % and 91.31 % accuracy. A simulation study has also demonstrated a comparative analysis with different convolution and attention networks for anomaly detection. This study proposes an effective video processing scheme with wide practical potential. The study also provides a new perspective and methodological basis for future research and applications in related fields.

Intelligent video surveillance systems with anomaly detection capabilities are indispensable for outdoor security. Video anomaly detection (VAD) is usually performed by learning patterns representing normal events and declaring an anomaly when an abnormal pattern is encountered. However, the features of normal patterns in a video often vary with time as real-world videos are non-stationary in nature, which makes its handling essential during VAD. To this end, we propose an approach for anomaly detection in videos, where a novel deep temporally recursive differencing network (DDN) diminishes the adverse effects of the non-stationary nature on VAD. The DDN consists of multiple layers of differencing operators of optimized orders, where every two consecutive layers are separated by a suitable nonlinearity. Spatial and temporal features are extracted from nonoverlapping blocks in video frames and fed to the DDN. While the spatial feature is obtained using a pretrained network, our temporal feature computation involves the use of FlowNetS with a new training strategy that does not require ground truth. The features at the output of DDN are used in a predictor based on autoregression and moving average of the regression errors. Then, the predictor's output estimates are compared to the corresponding actual values for anomaly detection, which also involves block-level selection and consistency check. Qualitative evaluation and quantitative comparison with several existing approaches on multiple standard datasets demonstrate the effectiveness of the proposed VAD approach. An ablation study highlighting the significance of the various components of our approach and a hyperparameter analysis are also provided.

Anomaly detection in videos is the task of identifying frames from a video sequence that depict events that do not conform to expected behavior, which is an extremely challenging task due to the ambiguous and unbounded properties of anomalies. With the development of deep learning, video anomaly detection methods based on deep neural networks have made great progress. The existing methods mainly follow two routes, namely, frame reconstruction and frame prediction. Due to the powerful generalization ability of neural networks, the application of reconstruction-based methods is limited. Recently, anomaly detection methods based on prediction have achieved advanced performance. However, their performance suffers when they cannot guarantee lower prediction errors for normal events. In this paper, we propose a novel future frame prediction model based on a bidirectional retrospective generation adversarial network (BR-GAN) for anomaly detection. To predict a future frame with higher quality for normal events, first, we propose a bidirectional prediction combined with a retrospective prediction method to fully mine the bidirectional temporal information between the predicted frame and the input frame sequence. Then, the intensity and gradient loss between the predicted frame and the actual frame together with an adversarial loss are used for appearance (spatial) constraints. In addition, we propose a sequence discriminator composed of a 3-dimensional (3D) convolutional neural network to capture the long-term temporal relationships between frame sequences composed of predicted frames and input frames; this network plays a crucial role in maintaining the motion (temporal) consistency of the predicted frames for normal events. Such appearance and motion constraints further facilitate future frame prediction for normal events, and thus, the prediction network can be highly capable of distinguishing normal and abnormal patterns. Extensive experiments on benchmark datasets demonstrate that our method outperforms most existing state-of-the-art methods, validating the effectiveness of our method for anomaly detection.

This paper aims to address the problem of behavioural anomaly detection in surveillance videos. We propose a novel framework tailored towards global video behaviour anomaly detection in complex outdoor scenes involving multiple temporal processes caused by correlated behaviours of multiple objects. Specifically, given a complex wide-area scene that has been segmented automatically into semantic regions where behaviour patterns are represented as discrete local atomic events, we formulate a novel Cascade of Dynamic Bayesian Networks (CasDBNs) to model behaviours with complex temporal correlations by utilising combinatory evidences collected from local atomic events. Using a cascade configuration not only allows for accurate detection of video behaviour anomalies, more importantly, it also improves the robustness of the model in dealing with the inevitable presence of errors and noise in the behaviour representation resulting less false alarms. We evaluate the effectiveness of the proposed framework on a real world traffic scene. The results demonstrate that the framework is able to detect not only anomalies that are visually obvious, but also those that are ambiguous or supported only by very weak visual evidence, e.g. those that can be easily missed by a human observer.

Several computer vision algorithms have been proposed to detect anomalous activities (robberies, murders, vandalism, among others) in videos. According to the learning approach, they can be classified into probabilistic distribution modeling, sparse coding, and deep learning based methods. The main drawbacks of these approaches are (i) extraction of low-level features that do not capture complex behaviors of instances on the scene, (ii) generation of features from irrelevant regions, (iii) overlooking of relationships among objects, and (iv) omission of long-term dependencies. To solve these issues, we propose a deep learning architecture that leverages the relationships among objects. It achieves this by using an attention mechanism and learning long-term dependencies using a multilayer recurrent neural network (multilayer LSTM). An AUC score of 0.749 on the UCF-Crime dataset confirms that the proposed algorithm competes effectively against several state-of-the-art approaches for anomaly detection in surveillance videos. It also explains the relationship between regions in the video frames and the anomaly detections.

Event detection in crowded surveillance videos is a challenging yet important problem. In this paper, we present our eSur (Event detection system on SURveillance video) system, which is derived from TRECVid'12 surveillance tasks. Currently, eSur attempts to detect two categories of events: 1) pair-wise events (e.g., PeopleMeet, PeopleSplitUp and Embrace); 2) action-like events (e.g., ObjectPut, CellToEar, PersonRuns and Pointing). In eSur system, we first employ people detection and tracking algorithms to locate target persons in 3D space-time domain. Then the video sequences in which target persons occur are partitioned into several spatio-temporal cubes. Visual features (i.e. cubic feature and MoSIFT) are computed over these cubes. After that, a sequence learning method, (namely SVM with dynamic time alignment kernel), is employed to infer the existence of an event for the video sequence. According to the TRECVid SED formal evaluation, eSur has yielded fairly encouraging results on TRECVid'12 dataset.

Video anomaly detection is of great significance due to its wide applications in video surveillance. Recently, there is a trend of using a video prediction framework to tackle this problem. This kind of methods detects anomalies according to the difference between a predicted frame and its ground truth. However, existing prediction methods lack the consideration of multi-scale temporal constraints on generating future frames. Therefore, based on the prediction framework, this paper proposes a temporal enhanced anomaly detection approach, which designs a generative adversarial network with dual discriminator (frame discriminator and sequence discriminator) to predict future frames. In order to obtain more realistic predictions, other than commonly used spatial constraints and the adversarial penalty from the frame discriminator, we also consider both short-range and long-range motions to impose constraints. Specifically, for short-range motion modeling, we utilize the optical flow loss to ensure temporal continuity over two adjacent frames, while for long-range motion modeling, we design a sequence discriminator to identify fake contained sequences from real sequences, making frames more consistent with their previous consecutive frames. Experiments on three datasets, UCSD Ped1, UCSD Ped2 and Avenue, demonstrate the effectiveness of our method in terms of various evaluation criteria for video anomaly detection.

Video anomaly detection (VAD) is of great importance for a variety of real-time applications in video surveillance. Most deep learning-based anomaly detection algorithms adopt a one-class learning scheme to train a classifier using only normal data to distinguish between normal and abnormal events during the test phase. However, these methods, whether they are reconstruction or prediction models, commonly face the challenge of the model’s overly strong representation capability, which leads to excessive fitting of abnormal events and thus limits the performance of the model in diverse scenarios. To address these challenges, this work develops a novel residual autoencoder and generative adversarial network-based 3-D convolutional network, called READ3D-net, for anomaly detection. An adaptive multimodal pseudoanomaly generator is developed to simulate and generate diverse pseudoanomalies, aiming to enhance the model’s ability to extract the features with regard to “abnormal” behaviors while reducing the interference of background on the detection performance. In addition, residual structures are incorporated into the design of a reconstruction-based autoencoder model to enhance its feature extraction and discriminative capabilities. To further improve the model’s reconstruction ability on normal data, a dynamic generative adversarial strategy is proposed for effective feature learning. Extensive experiments conducted on public benchmark datasets demonstrate that the proposed model is more competitive than the state-of-the-art methods in video anomaly detection tasks, fully validating the effectiveness and practicality of the proposed approach.

This paper presents a novel hybrid approach for real-time anomaly detection in video surveillance systems by integrating YOLOv8 object detection with advanced motion-based analysis techniques. The proposed system addresses critical limitations of existing single-modality detection methods through innovative fusion of deep learning and temporal analysis. The architecture incorporates parallel processing pipelines for YOLOv8 detection and optical flow computation, combined with an isolation forest-based anomaly decision framework that leverages historical detection patterns. Experimental evalution on a custom dataset of 5,000 surveillance video clips demonstrates superior performance with 92.3% accuracy, 89.7% precision, 94.1% recall, and 91.8% F1-score, while maintaining real-time processing at 30 FPS. The system significantly outperforms traditional approaches with 15.2% accuracy improvement over YOLO-only methods and 18.7% improvement over motion-only techniques. The proposed hybrid framework provides robust anomaly detection capabilities suitable for practical deployment in security-critical surveillance applications with reduced false positive rates and enhanced temporat consistence.

Anomaly detection plays a critical role in intelligent video surveillance. However, real-world video data obtained always contains large numbers of normal video data, along with large numbers of unlabeled data. A promising solution with one-class classification and semi-supervised learning may not be satisfactory as they fail to make good use of unlabeled data with only normal data available. In this paper, we introduced a new framework, called Positive Unlabeled learning-based Anomaly event Detection (PU-AD), to exploit the weakly-supervised information. To the best of our knowledge, this is the first work that introduces the PU idea and achieves detecting abnormal events with a limited number of partially labeled data. Experiments on real-world surveillance videos show that the proposed method outperforms the existing state-of-the-art methods.

We propose a novel unsupervised approach based on a two-stage object-centric adversarial framework that only needs object regions for detecting frame-level local anomalies in videos. The first stage consists in learning the correspondence between the current appearance and past gradient images of objects in scenes deemed normal, allowing us to either generate the past gradient from current appearance or the reverse. The second stage extracts the partial reconstruction errors between real and generated images (appearance and past gradient) with normal object behaviour, and trains a discriminator in an adversarial fashion. In inference mode, we employ the trained image generators with the adversarially learned binary classifier for outputting region-level anomaly detection scores. We tested our method on four public benchmarks, UMN, UCSD, Avenue and ShanghaiTech and our proposed object-centric adversarial approach yields competitive or even superior results compared to state-of-the-art methods.

During past few years’ significant usage of video monitoring has been observed in several places. Video monitoring has always helped in improvement of security specific aspects. These systems provide major helping hand in monitoring through multiple object detection methods. However, there are certain challenges related to weather conditions which needs to be addressed. In view of this in this paper, we study multiple object detection in surveillance videos through hybrid deep gaussian mixture ensembles. This method integrates multiple deep gaussian mixture components to form ensembles which helps to develop multiple object detection solutions in surveillance videos. The computational system consists of ensemble of three major deep gaussian mixture components. The ensemble pipeline is formed with augmenting, bagging and stacking methods. The ensemble consists of hybrid object detection paradigm with steps background analysis, video pre-processing, data integration, object detection, foreground detection and post-processing. The computational pipeline is successfully experimented with ViSOR and CDnet 2014 benchmarked datasets. All results are validated with accuracy, precision, sensitivity or recall, specificity, F1-Score and RMSE metrics. Several comparative studies are performed with state-of-the-art methods as well as baselines. The experimental results demonstrate superiority of this method in comparison with other methods. Ablation studies have also been performed here with superior results. This system presents robustness in multiple object detection for real life challenges such as sudden illumination variation, shadow presence, long term occlusion and formation of dynamic backgrounds. It provides cost-effective, more profitable, efficient and sustainable real time multiple object detection solutions

Anomaly detection in traffic videos has been recently gaining attention due to its importance in intelligent transportation systems. Due to several factors such as weather, viewpoint, lighting conditions, etc. affecting the video quality of a real time traffic feed, it still remains a challenging problem. Even though the performance of state-of-the-art methods on the available benchmark dataset has been competitive, they demand a massive amount of external training data combined with significant computational resources. In this paper, we propose a fast unsupervised anomaly detection system comprising of three modules: preprocessing module, candidate selection module and backtracking anomaly detection module. The preprocessing module outputs stationary objects detected in a video. Then, the candidate selection module removes the misclassified stationary objects using a nearest neighbor approach and then uses K-means clustering to identify potential anomalous regions. Finally, the backtracking anomaly detection algorithm computes a similarity statistic and decides on the onset time of the anomaly. Experimental results on the Track 4 test set of the NVIDIA AI CITY 2020 challenge show the efficacy of the proposed framework as we achieve an F1-score of 0.5926 along with 8.2386 root mean square error (RMSE) and are ranked second in the competition.

Fast fire flame detection in surveillance video using logistic regression and temporal smoothing