Event Detection In Crowded Scenes Research Articles

Abnormal event detection in surveillance videos based on low-rank and compact coefficient dictionary learning

In this paper, a novel approach to abnormal event detection in crowded scenes is presented based on a new low-rank and compact coefficient dictionary learning (LRCCDL) algorithm. First, based on the background subtraction and binarization of surveillance videos, we construct a feature space by extracting the histogram of maximal optical flow projection (HMOFP) feature of the foreground from a normal training frame set. Second, in the training stage, a new joint optimization of the nuclear-norm and l2, 1-norm is applied to obtain a compact coefficient low-rank dictionary. Third, in the detection stage, l2, 1-norm optimization is utilized to obtain the reconstruction coefficient vectors of the testing samples. Note that the l2, 1-norm forces the reconstruction vectors of all the testing samples to compactly surround the same center in the training stage, such that the reconstruction errors of abnormal testing samples are different from those of normal ones. Finally, a reconstruction cost (RC) is introduced to detect abnormal frames. Experimental results on both global and local abnormal event detection show the effectiveness of our algorithm. Based on comparisons with state-of-the-art methods employing various criteria, the proposed algorithm achieves comparable detection results.

Unsupervised deep learning system for local anomaly event detection in crowded scenes

Anomaly detection in video surveillance is a significant research subject because of its immense use in real-time applications. These days, open spots like hospitals, traffic areas, airports are monitored by video surveillance cameras. Strange occasions in these recordings have alluded to the anomaly. Unsupervised anomaly detection in the video be endowed with many challenges as there is no exact definition of abnormal events. It varies as for various situations. This paper aims to propose an effective unsupervised deep learning framework for video anomaly detection. Raw image sequences are combined with edge image sequences and given as input to the convolutional auto encoder-ConvLSTM model. Experimental evaluation of the proposed work is performed in three different benchmark datasets such as Avenue, UCSD ped1 and UCSD ped2. The proposed method Hybrid Deep Learning framework for Video Anomaly Detection (HDLVAD) reaches better accuracy compared to existing methods. Investigating video streaming in big data is our further research work.

Kernel null-space-based abnormal event detection using hybrid motion information

Abnormal event detection in crowded scenes is a challenging task in the computer vision community. A hybrid motion descriptor named the multiscale histogram of first- and second-order motion is proposed for abnormal event detection. The second-order motion describes the change in motion and is extracted by optical flow-based instantaneous tracking, which avoids object tracking in crowded scenes. For the modeling of normal events, a kernel null Foley–Sammon transform (KNFST) is introduced. KNFST makes a projection in the null space, where normal samples of all types are treated jointly instead of considering each known class individually. Experiments conducted on two benchmark datasets and comparisons to state-of-the-art methods demonstrate the superiority of the proposed method.

Abnormal event detection in crowded scenes using histogram of oriented contextual gradient descriptor

Detecting abnormal events in crowded scenes is an important but challenging task in computer vision. Contextual information is useful for discovering salient events in scenes; however, it cannot be characterized well by commonly used pixel-based descriptors, such as the HOG descriptor. In this paper, we propose contextual gradients between two local regions and then construct a histogram of oriented contextual gradient (HOCG) descriptor for abnormal event detection based on the contextual gradients. The HOCG descriptor is a distribution of contextual gradients of sub-regions in different directions, which can effectively characterize the compositional context of events. We conduct extensive experiments on several public datasets and compare the experimental results using state-of-the-art approaches. Qualitative and quantitative analysis of experimental results demonstrate the effectiveness of the proposed HOCG descriptor.

Abnormal event detection in crowded scenes using one-class SVM

In this paper, a new method for detecting abnormal events in public surveillance systems is proposed. In the first step of the proposed method, candidate regions are extracted, and the redundant information is eliminated. To describe appearance and motion of the extracted regions, HOG-LBP and HOF are calculated for each region. Finally, abnormal events are detected using two distinct one-class SVM models. To achieve more accurate anomaly localization, the large regions are divided into non-overlapping cells, and the abnormality of each cell is examined separately. Experimental results show that the proposed method outperforms existing methods based on the UCSD anomaly detection video datasets.

Structured dictionary learning for abnormal event detection in crowded scenes

Abnormal event detection is now a widely concerned research topic, especially for crowded scenes. In recent years, many dictionary learning algorithms have been developed to learn normal event regularities, and have presented promising performance for abnormal event detection. However, they seldom consider the structural information, which plays important roles in many computer vision tasks, such as image denoising and segmentation. In this paper, structural information is explored within a sparse representation framework. On the one hand, we introduce a new concept named reference event, which indicates the potential event patterns in normal video events. Compared with abnormal events, normal ones are more likely to approximate these reference events. On the other hand, a smoothness regularization is constructed to describe the relationships among video events. The relationships consist of both similarities in the feature space and relative positions in the video sequences. In this case, video events related to each other are more likely to possess similar representations. The structured dictionary and sparse representation coefficients are optimized through an iterative updating strategy. In the testing phase, abnormal events are identified as samples which cannot be well represented using the learned dictionary. Extensive experiments and comparisons with state-of-the-art algorithms have been conducted to prove the effectiveness of the proposed algorithm.

Abnormal event detection in crowded scenes using two sparse dictionaries with saliency

Abnormal event detection in crowded scenes is a challenging problem due to the high density of the crowds and the occlusions between individuals. We propose a method using two sparse dictionaries with saliency to detect abnormal events in crowded scenes. By combining a multiscale histogram of optical flow (MHOF) and a multiscale histogram of oriented gradient (MHOG) into a multiscale histogram of optical flow and gradient, we are able to represent the feature of a spatial–temporal cuboid without separating the individuals in the crowd. While MHOF captures the temporal information, MHOG encodes both spatial and temporal information. The combination of these two features is able to represent the cuboid’s appearance and motion characteristics even when the density of the crowds becomes high. An abnormal dictionary is added to the traditional sparse model with only a normal dictionary included. In addition, the saliency of the testing sample is combined with two sparse reconstruction costs on the normal and abnormal dictionary to measure the normalness of the testing sample. The experiment results show the effectiveness of our method.

Collective Representation for Abnormal Event Detection

Abnormal event detection in crowded scenes is a hot topic in computer vision and information retrieval community. In this paper, we study the problems of detecting anomalous behaviors within the video, and propose a robust collective representation with multi-feature descriptors for abnormal event detection. The proposed method represents different features in an identical representation, in which different features of the same topic will show more common properties. Then, we build the intrinsic relation between different feature descriptors and capture concept drift in the video sequence, which can robustly discriminate between abnormal events and normal events. Experimental results on two benchmark datasets and the comparison with the state-of-the-art methods validate the effectiveness of our method.

Saliency-based abnormal event detection in crowded scenes

Abnormal event detection plays a critical role for intelligent video surveillance, and detection in crowded scenes is a challenging but more practical task. We present an abnormal event detection method for crowded video. Region-wise modeling is proposed to address the inconsistent detected motion of the same object due to different depths of field. Comparing to traditional block-wise modeling, the region-wise method not only can reduce heavily the number of models to be built but also can enrich the samples for training the normal events model. In order to reduce the computational burden and make the region-based anomaly detection feasible, a saliency detection technique is adopted in this paper. By identifying the salient parts of the image sequences, the irrelevant blocks are ignored, which removes the disturbance and improves the detection performance further. Experiments on the benchmark dataset and comparisons with the state-of-the-art algorithms validate the advantages of the proposed method.

Statistical Hypothesis Detector for Abnormal Event Detection in Crowded Scenes.

Abnormal event detection is now a challenging task, especially for crowded scenes. Many existing methods learn a normal event model in the training phase, and events which cannot be well represented are treated as abnormalities. However, they fail to make use of abnormal event patterns, which are elements to comprise abnormal events. Moreover, normal patterns in testing videos may be divergent from training ones, due to the existence of abnormalities. To address these problems, in this paper, an abnormality detector is proposed to detect abnormal events based on a statistical hypothesis test. The proposed detector treats each sample as a combination of a set of event patterns. Due to the unavailability of labeled abnormalities for training, abnormal patterns are adaptively extracted from incoming unlabeled testing samples. Contributions of this paper are listed as follows: 1) we introduce the idea of a statistical hypothesis test into the framework of abnormality detection, and abnormal events are identified as ones containing abnormal event patterns while possessing high abnormality detector scores; 2) due to the complexity of video events, noise seldom follows a simple distribution. For this reason, we approximate the complex noise distribution by employing a mixture of Gaussian. This benefits the modeling of video events and improves abnormality detection accuracies; and 3) because of the existence of abnormalities, there are always some unusually occurring normal events in the testing videos, which differ from the training ones. To represent normal events precisely, an online updating strategy is proposed to cover these cases in the normal event patterns. As a result, false detections are eliminated mostly. Extensive experiments and comparisons with state-of-the-art methods verify the effectiveness of the proposed algorithm.

Abnormal event detection in crowded scenes based on deep learning

In this paper, we propose to use the deep learning technique for abnormal event detection by extracting spatiotemporal features from video sequences. Human eyes are often attracted to abnormal events in video sequences, thus we firstly extract saliency information (SI) of video frames as the feature representation in the spatial domain. Optical flow (OF) is estimated as an important feature of video sequences in the temporal domain. To extract the accurate motion information, multi-scale histogram optical flow (MHOF) can be obtained through OF. We combine MHOF and SI into the spatiotemporal features of video frames. Finally a deep learning network, PCANet, is adopted to extract high-level features for abnormal event detection. Experimental results show that the proposed abnormal event detection method can obtain much better performance than the existing ones on the public video database.

Abnormal event detection in crowded scenes via bag-of-atomic-events-based topic model

In this paper, we propose a novel framework for abnormal event detection in crowded scenes. A new concept of atomic event is introduced into this framework, which is the basic component of video events. Different from previous bag-of-words (BoW) modeling-based methods that represent feature descriptors using only one code word, a feature descriptor is represented using a few more atomic events in bag-of-atomic-ev (BoAE) modeling. Consequently, the approximation error is reduced by using the obtained BoAE representation. In the context of abnormal event detection, BoAE representation is more suitable to describe abnormal events than BoW representation, because the abnormal event may not correspond to any code word in BoW modeling. Fast latent Dirichlet allocation is adopted to learn a model of normal events, as well as classify the testing event with low likelihood under the learned model. Our proposed framework is robust, computationally efficient, and highly accurate. We validate these advantages by conducting extensive experiments on several challenging datasets. Qualitative and quantitative results show the promising performance compared with other state-of-the-art methods.

Real-time video event detection in crowded scenes using MPEG derived features: A multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

Sparse representation for robust abnormality detection in crowded scenes

In crowded scenes, the extracted low-level features, such as optical flow or spatio-temporal interest point, are inevitably noisy and uncertainty. In this paper, we propose a fully unsupervised non-negative sparse coding based approach for abnormality event detection in crowded scenes, which is specifically tailored to cope with feature noisy and uncertainty. The abnormality of query sample is decided by the sparse reconstruction cost from an atomically learned event dictionary, which forms a sparse coding bases. In our algorithm, we formulate the task of dictionary learning as a non-negative matrix factorization (NMF) problem with a sparsity constraint. We take the robust Earth Mover's Distance (EMD), instead of traditional Euclidean distance, as distance metric reconstruction cost function. To reduce the computation complexity of EMD, an approximate EMD, namely wavelet EMD, is introduced and well combined into our approach, without losing performance. In addition, the combination of wavelet EMD with our approach guarantees the convexity of optimization in dictionary learning. To handle both local abnormality detection (LAD) and global abnormality detection, we adopt two different types of spatio-temporal basis. Experiments conducted on four public available datasets demonstrate the promising performance of our work against the state-of-the-art methods.

Motion feature filtering for event detection in crowded scenes

We describe a spatio-temporal feature filtering approach that is appropriate for detecting video events in public scenes containing from many to few people. This non-discrete tracking – or pattern flow analysis – is distinguished by the fact that the usual video processing step of object segmentation is omitted; instead motion features alone are used to detect, follow, and separate activity. Motion features include location, scale, score (magnitude), direction, and velocity. The method entails gradient-based motion detection and multiscale motion feature calculation to obtain a scene activity vector. We focus on obtaining these motion features and filtering them to obtain information on activity, with the end-goal being event detection, classification, and anomaly detection. Examples of information extraction we show in this paper include: distinguishing anomalous from trend activity via shape of the activity profile over time, detecting event onset and direction of people flow using direction (and feature confidence) values, and measuring the periodicity of similar activity from magnitude values over time. We demonstrate utility of the approach on 3 video datasets: hallway, emergency event, and subway platform.

Abnormal event detection in crowded scenes using sparse representation

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given a collection of normal training examples, e.g., an image sequence or a collection of local spatio-temporal patches, we propose the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. To condense the over-completed normal bases into a compact dictionary, a novel dictionary selection method with group sparsity constraint is designed, which can be solved by standard convex optimization. Observing that the group sparsity also implies a low rank structure, we reformulate the problem using matrix decomposition, which can handle large scale training samples by reducing the memory requirement at each iteration from O(k2) to O(k) where k is the number of samples. We use the columnwise coordinate descent to solve the matrix decomposition represented formulation, which empirically leads to a similar solution to the group sparsity formulation. By designing different types of spatio-temporal basis, our method can detect both local and global abnormal events. Meanwhile, as it does not rely on object detection and tracking, it can be applied to crowded video scenes. By updating the dictionary incrementally, our method can be easily extended to online event detection. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our method.