The detection of abnormal behaviours in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection. Experimental results on two benchmarks suggest that detection and localization of the proposed method outperforms existing methods in terms of accuracy.
@cite_38: We present a novel approach for video parsing and simultaneous online learning of dominant and anomalous behaviors in surveillance videos. Dominant behaviors are those occurring frequently in videos and hence, usually do not attract much attention. They can be characterized by different complexities in space and time, ranging from a scene background to human activities. In contrast, an anomalous behavior is defined as having a low likelihood of occurrence. We do not employ any models of the entities in the scene in order to detect these two kinds of behaviors. In this paper, video events are learnt at each pixel without supervision using densely constructed spatio-temporal video volumes. Furthermore, the volumes are organized into large contextual graphs. These compositions are employed to construct a hierarchical codebook model for the dominant behaviors. By decomposing spatio-temporal contextual information into unique spatial and temporal contexts, the proposed framework learns the models of the dominant spatial and temporal events. Thus, it is ultimately capable of simultaneously modeling high-level behaviors as well as low-level spatial, temporal and spatio-temporal pixel level changes.
@cite_18: In this paper, we propose a fast and robust framework for anomaly detection in crowed scenes. In our method, anomaly is adaptively modeled as a deviation from the normal behavior of crowd observed in the scene. For this purpose, we extract motion features by repeatedly initializing a grid of particles over a temporal window. These features are exploited in a real-time anomaly detection system. In order to model the ordinary behavior of the people moving in the crowd, we use the Gaussian mixture model (GMM) technique, which is robust enough to capture the scene dynamics. As opposed to explicitly modeling the values of all the pixels as a mixture of Gaussians, we adopted the GMM to learn the behavior of the motion features extracted from the particles. Based on the persistence and the variance of each Gaussian distribution, we determine which Gaussians can be associated to the normal behavior of the crowd. Particles with motion features that do not fit the distributions representing normal behavior are signaled as anomaly, until there is a Gaussian able to include them with sufficient evidence supporting it. Experiments are extensively conducted on publically available benchmark dataset, and also on a challenging dataset of video sequences we captured. The experimental results revealed that the proposed method performs effectively for anomaly detection.
@cite_4: In this paper we propose a novel approach to detect anomalies in crowded scenes. This is achieved by analyzing the crowd behavior by extracting the corner features. For each corner feature we collect a set of motion features. The motion features are used to train an MLP neural network during the training stage, and the behavior of crowd is inferred on the test samples. Considering the difficulty of tracking individuals in dense crowds due to multiple occlusions and clutter, in this work we extract corner features and consider them as an approximate representation of the people motion. Corner features are then advected over a temporal window through optical flow tracking. Corner features well match the motion of individuals and their consistency, and accuracy is higher both in structured and unstructured crowded scenes compared to other detectors. In the current work, corner features are exploited to extract motion information, which is used as input prior to train the neural network. The MLP neural network is subsequently used to highlight the dominant corner features that can reveal an anomaly in the crowded scenes. The experimental evaluation is conducted on a set of benchmark video sequences commonly used for crowd motion analysis. In addition, we show that our approach outperforms a state of the art technique proposed in.
@cite_57: Video anomaly detection plays a critical role for intelligent video surveillance. We present an abnormal video event detection system that considers both spatial and temporal contexts. To characterize the video, we first perform the spatio-temporal video segmentation and then propose a new region-based descriptor called “Motion Context,” to describe both motion and appearance information of the spatio-temporal segment. For anomaly measurements, we formulate the abnormal event detection as a matching problem, which is more robust than statistic model-based methods, especially when the training dataset is of limited size. For each testing spatio-temporal segment, we search for its best match in the training dataset, and determine how normal it is using a dynamic threshold. To speed up the search process, compact random projections are also adopted. Experiments on the benchmark dataset and comparisons with the state-of-the-art methods validate the advantages of our algorithm.
@cite_47: This paper presents an approach for detecting suspicious events in videos by using only the video itself as the training samples for valid behaviors. These salient events are obtained in real-time by detecting anomalous spatio-temporal regions in a densely sampled video. The method codes a video as a compact set of spatio-temporal volumes, while considering the uncertainty in the codebook construction. The spatio-temporal compositions of video volumes are modeled using a probabilistic framework, which calculates their likelihood of being normal in the video. This approach can be considered as an extension of the Bag of Video words (BOV) approaches, which represent a video as an order-less distribution of video volumes. The proposed method imposes spatial and temporal constraints on the video volumes so that an inference mechanism can estimate the probability density functions of their arrangements. Anomalous events are assumed to be video arrangements with very low frequency of occurrence. The algorithm is very fast and does not employ background subtraction, motion estimation or tracking. It is also robust to spatial and temporal scale changes, as well as some deformations. Experiments were performed on four video datasets of abnormal activities in both crowded and non-crowded scenes and under difficult illumination conditions. The proposed method outperformed all other approaches based on BOV that do not account for contextual information.
@cite_25: In this paper, rather than modeling activities in videos individually, we propose a hierarchical framework that jointly models and recognizes related activities using motion and various context features. This is motivated from the observations that the activities related in space and time rarely occur independently and can serve as the context for each other. Given a video, action segments are automatically detected using motion segmentation based on a nonlinear dynamical model. We aim to merge these segments into activities of interest and generate optimum labels for the activities. Towards this goal, we utilize a structural model in a max-margin framework that jointly models the underlying activities which are related in space and time. The model explicitly learns the duration, motion and context patterns for each activity class, as well as the spatio-temporal relationships for groups of them. The learned model is then used to optimally label the activities in the testing videos using a greedy search method. We show promising results on the VIRAT Ground Dataset demonstrating the benefit of joint modeling and recognizing activities in a wide-area scene.
@cite_47 , an extension of the bag of video words (BOV) approach is used by A context-aware anomaly detection algorithm is proposed in @cite_25 , where the authors represent the video using motions and the context of videos. @cite_57 , a method for modeling both motion and shape with respect to a descriptor (named motion context'') is proposed; they consider anomaly detection as a matching problem. @cite_38 introduce a method for learning the events of a video by using the construction of a hierarchical codebook for dominant events in a video. Ullah et al @cite_18 learn an MLP neural network using trained particles to extract the video behavior. A Gaussian mixture model (GMM) is exploited for learning the behavior of particles using extracted features. In addition, in @cite_4 , an MLP neural network for extracting the corner features from normal training samples is proposed; authors also label the test samples using that MLP.