Detecting anomalies in crowded scenes via locality-constrained affine subspace coding

Anomaly detection in crowd scene has attracted an increasing attention in video surveillance, but a precise detection still remains a challenge. This paper presents a novel online learning method to automatically detect abnormal behaviors in crowd scene. Our focus is mainly on the deviation between the real motion and the predicted one. Through online defining experts, analyzing their motions, and dynamically updating the learned model, anomaly can be identified by the final expert joint decision. The outputs are represented as the anomaly probability of an examined frame. Compared with most of existing methods, the proposed one needs neither tracking each individual straight to the end nor requires any complex training procedure. We test the proposed method on public datasets, and the results show its effectiveness.

Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies. In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes. Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements. Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns. Then a multimodal fusion scheme is utilized to learn the deep representation of crowd patterns. Based on the learned deep representation, a one-class support vector machine model is used to detect anomaly events. The proposed method is evaluated using two available public datasets and compared with state-of-the-art methods. The experimental results show its competitive performance for anomaly event detection in video surveillance.

A new approach is proposed to analysis and localize abnormal events in crowded scenes, such us detecting suspicious behavior, sudden assemble and dispersion of pedestrian in the crowd scenes. The proposed methodology uses streak flow technique along with LDA (Latent Dirichlet Allocation)to detect anomalies in crowd scenes. This method divides the frames into blocks for accurate representation of spatial and temporal changes in scene. Optical flow algorithm is used to estimate the motion and direction of the pedestrian in crowd scene. Crowd flow motion is extracted using streak flow with the combination of streak line and potential functions. Experimental result shows that the detection of abnormalities in crowd scenes using streak flow approach gives better accuracy of 93.37% than the state of Art of techniques.

These days consciousness about public safety increases and CCTV cameras are installed at almost all public places. But generally automatic smart surveillance systems are not available. In this manuscript, emphasis is given to detect and classify abnormal events in surveillance video especially in crowd environments. Abnormal event detection is a challenging task because the definition of abnormality is subjective. A normal event in one situation can be considered an abnormal event in another case. In the surveillance video with a dense crowd, automatic anomaly detection becomes very difficult because of clutter and severe occlusion. This manuscript represents CNN (Convolutional Neural Network) and CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) based approaches for detection and classification of abnormal events. The CNN architecture is developed from scratch and can be used for spatial domains. LSTM architecture is developed for the temporal domain. Feature sequences are generated using CNN model and given as input to LSTM model. Experiments are carried out using five different publicly available benchmark datasets. The performance is measured by accuracy and area under the ROC (receiver operating characteristic) curve (AUC). CNN-LSTM approach works better than only CNN.

In this paper, we propose a novel method for global abnormal events detection in crowded scenes. Each video is described as the set of overlapping space-time cubes. The histogram of optical flow orientation and motion magnitude are used as global feature descriptor to capture the motion magnitude and orientation of the normal and abnormal events. The motion-rich space-time cubes are selected to enhance the computational speed of the algorithm and omit background subtraction step, forms the proposed approach novel and unique. Simple and cost-effective one-class SVM classifier is used to distinguish between normal and abnormal events in a video. The one-class SVM classifier is trained with normal events from the training data. The proposed method is tested on benchmark UMN and PETS2009 dataset. The improved results are reported to evaluate the performance of the proposed method.

Human activities that deviate from the norm are deemed abnormal, and such individuals are referred to as anomalous objects. Employing visual data to detect abnormal behaviour is a complex topic in video processing. This research proposes a novel method for detecting abnormal behaviour in complicated, crowded environments. In this article, we proposed a robust method for abnormal action recognition. We initially processed the data, applying fuzzy c mean and super pixel-based segmentation, extracting the features and tracking the object. The next step is to optimize the data. We used a deep data mining approach via t-distributed stochastic neighbor embedding procedure, and for classification, we applied random forest. We achieved 80.24% accuracy rate for human detection over UCSD dataset, and 79.19% for Shanghai tech dataset. We also got 84.00% accuracy of abnormal action recognition over UCSD dataset and 82.00% over Shanghai tech dataset.

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.

Scene changes that typically occur in a real-world setting degrade anomaly detection performance over the long run. Most of the existing methods ignore the challenge of temporal concept drift in video surveillance. In this paper, we propose an unsupervised end-to-end framework for adaptive scene level anomaly detection. We utilize multivariate Gaussian mixtures for adaptive scene learning. The mixture represents the possible distribution of normal and abnormal events shown till now. The distribution adapts itself according to the slow scene changes. We introduce a Mahalanobis distance-based contribution factor to update mixture parameters on the arrival of each new event. A detailed discussion and experiments are conducted to decide optimum local as well as global temporal context. The existing public datasets for anomaly detection are of very short duration (maximum of 1.5 hours) to be used for evaluating adaptive approaches. Therefore we also collected a longer duration dataset of continuous 10 hours duration. We achieved a promising performance of 85.14% AUC and 21.26% EER on this data.

Modelling events in densely crowded environments remains challenging, due to the diversity of events and the noise in the scene. We propose a novel approach for anomalous event detection in crowded scenes using dynamic textures described by the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) descriptor. The scene is divided into spatio-temporal patches where LBP-TOP based dynamic textures are extracted. We apply hierarchical Bayesian models to detect the patches containing unusual events. Our method is an unsupervised approach, and it does not rely on object tracking or background subtraction. We show that our approach outperforms existing state of the art algorithms for anomalous event detection in UCSD dataset.

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

Ⅰ. Abstract In recent years, cc-tv cameras are used in various locations. The data captured by these cameras can be used for event prediction, real-time monitoring, and goal-oriented analysis, including anomaly and intrusion detection. With advancements in Artificial Intelligence, several methods are applied for anomaly detection, in which convolutional neural networks (CNNs) powered by deep-learning have improved detection accuracy. This article aims to introduce a novel deep learning- based approach for crime detection in video surveillance footage. this method has been tested on the UCSD dataset and has demonstrated improved accuracy in detecting criminal activities. Nowadays, various Artificial Intelligence techniques have been used to detect anomalies, amongst them convolutional neural networks using deep learning techniques improved the detection accuracy significantly. The goal of this article is to propose a new method based on deep learning techniques for Crime detection in video surveillance cameras. The proposed method has been evaluated in the UCSD dataset, and showed an increase in the accuracy of the Crime detection.

iSurveillance: Intelligent framework for multiple events detection in surveillance videos

Achieving reliable and safe autonomous driving in off-road environments requires accurate and efficient terrain traversability analysis. However, this task faces several challenges, including the scarcity of large-scale datasets tailored for off-road scenarios, the high cost and potential errors of manual annotation, the stringent real-time requirements of motion planning, and the limited computational power of onboard units. To address these challenges, this paper proposes a novel traversability learning method that leverages self-supervised learning, eliminating the need for manual annotation. We adopt bird's-eye view (BEV) as the input representation to better support ego-centric coordinate representation and integration with motion planning. During vehicle operation, the proposed method conducts online analysis of traversed regions and dynamically updates prototypes to adaptively assess the traversability of the current environment, effectively handling dynamic scene changes. We evaluate our approach against state-of-the-art benchmarks on both public datasets and our own dataset, covering diverse seasons and geographical locations. Experimental results demonstrate that our method achieves superior performance over recent approaches in terms of Area Under the Receiver Operating Characteristic curve (AUROC), Average Precision (AP), and F1-score. Additionally, real-world vehicle experiments show that our method operates at 10 Hz, while a 5.5 km autonomous driving experiment further validates the generated traversability cost map's compatibility with downstream motion planning.

In this paper, we propose a new approach for recognizing group events and abnormality detection in a crowded scene. A manifold learning algorithm with temporal-constraints is proposed to embed a video of a crowded scene in a low-dimensional space. Our low dimensional representation of a video preserves the spatial temporal property of a video as well as the characteristic of the video. Recognizing video events and abnormality detection in a crowded scene is achieved by studying the video trajectory in the manifold space. We evaluate our proposed method on the state-of-the-art public data-sets containing different crowd events. Qualitative and quantitative results show the promising performance of the proposed method.

Pedestrian detection and counting gains an important role in video surveillance for entrance/exit monitoring, customer behavior analysis, traffic management and public service management. This paper proposes a detection based people counting approach that can easily be applied to all real-life scenarios and provides much accurate result as compared with those of other existing methods. The proposed approach is divided into two phases. In the first phase, object detection is performed while in the second phase, detected frames are analyzed based on their features to provide the count of the persons in a given video. We evaluate our approach on PETS 2009, Mall, UCSD datasets. An additional dataset(Traffic dataset) is also used in order to verify the effectiveness of our model for multi-object counting. Experimental results on these datasets justifies the good performance of our proposed method for crowd counting.1