Learning Video Manifolds for Content Analysis of Crowded Scenes

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.

Crowd scene analysis is the certification tasks in crowded scene understanding. Crowd is a same or different set of people arranged in one group. Generally crowd form in the way of pedestrians, supermarket, and marathons. In this paper introduce Convolution Neural Networks and deep learning model is used for the analysis of crowd scene. In these paper propose, findings the number of people arrived in one group and also finds the crowd density map. People counting in extremely dense crowds are an important step for video surveillance and anomaly warning. The above mentioned works, Several problems becomes especially more challenging due to the lack of training samples, severe blockages, disorder scenes, and modification of perspective. In existing methods estimating crowd count using handcrafted features such as SIFTS and HOG. In current vision most suited method is to predict the better performance of estimating crowd density and crowd count based on deep learning network. Lucas kanade optical flow can finds the displacement vector between two consecutive frames. 3D volumes video slices can be arranged in sequential manner. In this crowd scene analysis represents convolutional crowd dataset as 100 videos from 800 crowd scenes and build an attribute set with 94 attributes.

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.

Abnormal behavior detection using hybrid agents in crowded scenes

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.

Crowd scene analysis receives growing attention due to its wide applications. Grasping the accurate crowd location is important for identifying high-risk regions. In this article, we propose a Compressed Sensing based Output Encoding (CSOE) scheme, which casts detecting pixel coordinates of small objects into a task of signal regression in encoding signal space. To prevent gradient vanishing, we derive our own sparse reconstruction backpropagation rule that is adaptive to distinct implementations of sparse reconstruction and makes the whole model end-to-end trainable. With the support of CSOE and the backpropagation rule, the proposed method shows more robustness to deep model training error, which is especially harmful to crowd counting and localization. The proposed method achieves state-of-the-art performance across four mainstream datasets, especially achieves excellent results in highly crowded scenes. A series of analysis and experiments support our claim that regression in CSOE space is better than traditionally detecting coordinates of small objects in pixel space for highly crowded scenes.

Crowd behavior recognition is becoming an important research topic in video surveillance for public places. In this paper, we first discuss the crowd feature selection and extraction and propose a multiple-frame feature point detection and tracking based on the KLT tracker. We state that behavior modelling of crowd is usually coarse compared to that for individuals. Instead of developing general crowd behavior models, we propose to model crowd events for specific end-user scenarios. As a result, a same type of event may be modelled slightly differently from one scenario to another and several models are to be defined. Consequently, fast modelling is required and this is enabled by the use of an extended Scenario Recognition Engine (SRE) in our approach. Crowd event models are defined; particularly, composite events accommodating evidence accumulation allow to increase detection reliability. Tests have been conducted on real surveillance video sequences containing crowd scenes. The crowd tracking algorithm proves to be robust and gives reliable crowd motion vectors. The crowd event detection on real sequences gives reliable results of a few common crowd behaviors by simple dedicated models.

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.

Abnormality detection in crowded scenes plays a very important role in automatic monitoring of surveillance feeds. Here we present a novel framework for abnormality detection in crowd videos. The key idea of the approach is that rarely or sparsely occurring events correspond to abnormal activities while the commonly occurring events correspond to the normal activities. Given an input video, multiple feature matrices are computed which are decomposed into their low-rank and sparse components, out of which the sparse components correspond to the abnormal activities. The approach does not require any explicit modeling of crowd behavior or training. Localization of the anomalies is obtained as a by-product of the proposed approach by doing an inverse mapping between the entries of the matrix and the pixels in the video frames. The method is very general and can be applied for both sparsely crowded as well as densely crowded scenes and it can be used to detect both global and local abnormalities. Experimental evaluation on two widely used datasets as well as some dense crowd videos downloaded from the web shows the effectiveness of the proposed approach. Comparison with several state-of-the-art crowd abnormality detection approaches show that the proposed method compares well as compared to the other approaches.

We propose a method to detect abnormal crowd behavior using integrated multiple behavior models. Traditional abnormal detection methods are based only on personal behavior models. However, a single behavior model cannot accurately reflect complex crowd behavior. To solve this problem, we introduce an integrated multiple behavior model for accurate abnormal behavior detection in a complex crowd scene. We use not only the personal behavior model, but also multiple social behavior models. The experimental results show that our proposed method efficiently detects the abnormal behavior in a crowded scene.

This paper presents a new inlier-based outlier detection scheme for the analysis of abnormal behavior in crowded scenes. First, we segment the video into a set of cubes, and then extract the three feature descriptors from each cube, including the histogram of oriented gradients (HOG), the histogram of motion directions, and the motion magnitude descriptors. Thereafter, for each feature descriptor the Kullback-Leibler importance estimation procedure (KLIEP) is employed to compute the ratio of test and training densities, referred to as the importance, instead of computing these two densities separately. This allows us to avoid the difficulty in estimating complex probability density estimation. The importance denotes an inlier score which represents the degree of similarity between the test and training data. Based on the importance of each descriptor and a prespecified threshold in each cube, we can then identify if a cube contains an anomaly event. Through computer simulations, we find that the new approach provides high accuracy of localization rate based on the widespread UCSD datasets compared with previous works.

In machine learning, a high dimensional dataset such as the digital image of human face is often considered as a point set distributed on a (differentiable) manifold. In many cases the intrinsic dimension of this manifold is highly lower than the representation dimension. In order to ease data processing, manifold-based dimension reduction algorithms were put forward since this century. The real purpose of manifold learning (MAL) is to learn a suitable metric in the low dimensional space. One main limitation of the existing MAL algorithms is that they all do not consider the intrinsic curvature of the embedded manifold, which means that the intrinsic geodesic distance cannot be uncovered by these algorithms. The intrinsic geodesic distance on the manifold is measured by Riemannian metric which is highly affected by Riemannian curvature. With this idea in mind, our work proposes to formulate a new algorithm by adding the curvature information to metric learning. We study a new curvature flow from input dataset. By employing this curvature flow, we obtain a Mahalanobis metric which can better uncover the intrinsic structure of the embedded manifold. To show the effectiveness of our proposed method, we compare our algorithm with several traditional MAL algorithms.

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.

Video analysis of crowded scenes is challenging due to the complex motion of individual people in the scene. The collective motion of pedestrians form a crowd flow, but individuals often largely deviate from it as they anticipate and react to each other. Deviations from the crowd decreases the pedestrian’s efficiency: a sociological concept that measures the difference of actual motion from the intended speed and direction. In this paper, we derive a novel method for estimating pedestrian efficiency from videos. We first introduce a novel crowd motion model that encodes the temporal evolution of local motion patterns represented with directional statistics distributions. This model is then used to estimate the intended motion of pedestrians at every space-time location, which enables visual measurement of the pedestrian efficiency. We demonstrate the use of this pedestrian efficiency to detect unusual events and to track individuals in crowded scenes. Experimental results show that the use of pedestrian efficiency leads to state-of-the-art accuracy in these critical applications.KeywordsAnomaly DetectionEmergent BehaviorIntended MotionSocial Force ModelCrowded SceneThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this paper, we target on the problem of estimating the statistic of pedestrian travel time within a period from an entrance to a destination in a crowded scene. Such estimation is based on the global distributions of crowd densities and velocities instead of complete trajectories of pedestrians, which cannot be obtained in crowded scenes. The proposed method is motivated by our statistical investigation into the correlations between travel time and global properties of crowded scenes. Active regions are created for each source-destination pair to model the probable walking regions over the corresponding source-destination traffic flow. Two sets of scene features are specially designed for modeling moving and stationary persons inside the active regions and their influences on pedestrian travel time. The estimation of pedestrian travel time provides valuable information for both crowd scene understanding and pedestrian behavior analysis, but was not sufficiently studied in literature. The effectiveness of the proposed pedestrian travel time estimation model is demonstrated through several surveillance applications, including dynamic scene monitoring, localization of regions blocking traffics, and detection of abnormal pedestrian behaviors. Many more valuable applications based on our method are to be explored in the future.