This paper proposes a method based on repulsive forces and sparse reconstruction for the detection and location of abnormal events in crowded scenes. In order to avoid the challenging problem of accurately tracking each specific individual in a dense or complex scene, we divide each frame of the surveillance video into a fixed number of grids and select a single representative point in each grid as the individual to track. The repulsive force model, which can accurately reflect interactive behaviors of crowds, is used to calculate the interactive forces between grid particles in crowded scenes and to construct a force flow matrix using these discrete forces from a fixed number of continuous frames. The force flow matrix, which contains spatial and temporal information, is adopted to train a group of visual dictionaries by sparse coding. To further improve the detection efficiency and avoid concept drift, we propose a fully unsupervised global and local dynamic updating algorithm, based on sparse reconstruction and a group of word pools. For anomaly location, since our method is based on a fixed grid, we can judge whether anomalies occur in a region intuitively according to the reconstruction error of the corresponding visual words. We experimentally verify the proposed method using the UMN dataset, the UCSD dataset and the Web dataset separately. The results indicate that our method can not only detect abnormal events accurately, but can also pinpoint the location of anomalies.
The most important task to describe crowd behaviors is to extract powerful features. At present, some local features, such as histograms of optical flow @cite_11 @cite_37 @cite_33 , spatial-temporal motion patterns @cite_2 , mixtures of dynamic textures @cite_21 and salient features @cite_23 have been widely adopted. As well as these local features, some researchers have also used global features to represent crowd behaviors. For example, @cite_34 applied a simplified social force model to describe crowd behaviors by estimating the interaction force between individuals. @cite_7 introduced streaklines integrated with a particle advection scheme that can incorporate the spatial changes in the particle flow. @cite_0 leveraged chaotic invariance to analyze events in both coherent and incoherent scenes. @cite_25 proposed an Appearance and Motion DeepNet (AMDN) method which utilizes deep neural networks to automatically learn feature representations. In @cite_47 , a potential interaction energy based on linear trajectory avoidance was proposed to represent the behavioral state of subjects.
@cite_37 @cite_33 @cite_7 @cite_21 @cite_0 @cite_23 @cite_2 @cite_47 @cite_34 @cite_25 @cite_11
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Approaches inspired by Newtonian mechanics have been successfully applied for detecting abnormal behaviors in crowd scenarios, being the most notable example the Social Force Model (SFM). This class of approaches describes the movements and local interactions among individuals in crowds by means of repulsive and attractive forces. Despite their promising performance, recent socio-psychology studies have shown that current SFM-based methods may not be capable of explaining behaviors in complex crowd scenarios. An alternative approach consists in describing the cognitive processes that gives rise to the behavioral patterns observed in crowd using heuristics. Inspired by these studies, we propose a new hybrid framework to detect violent events in crowd videos. More specifically, (i) we define a set of simple behavioral heuristics to describe people behaviors in crowd, and (ii) we implement these heuristics into physical equations, being able to model and classify such behaviors in the videos. The resulting heuristic maps are used to extract video features to distinguish violence from normal events. Our violence detection results set the new state of the art on several standard benchmarks and demonstrate the superiority of our method compared to standard motion descriptors, previous physics-inspired models used for crowd analysis and pre-trained ConvNet for crowd behavior analysis. In this paper we introduce a novel method to detect and localize abnormal behaviors in crowd videos using Social Force model. For this purpose, a grid of particles is placed over the image and it is advected with the space-time average of optical flow. By treating the moving particles as individuals, their interaction forces are estimated using social force model. The interaction force is then mapped into the image plane to obtain Force Flow for every pixel in every frame. Randomly selected spatio-temporal volumes of Force Flow are used to model the normal behavior of the crowd. We classify frames as normal and abnormal by using a bag of words approach. The regions of anomalies in the abnormal frames are localized using interaction forces. The experiments are conducted on a publicly available dataset from University of Minnesota for escape panic scenarios and a challenging dataset of crowd videos taken from the web. The experiments show that the proposed method captures the dynamics of the crowd behavior successfully. In addition, we have shown that the social force approach outperforms similar approaches based on pure optical flow. 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. A common phenomenon in everyday life is that, when a strange event occurs or is announced, a regular crowd can completely change, showing different intense emotions and sometimes uncontrollable and violent emerging behavior. These emotions and behaviors that disturb the organization of a crowd are of concern in our study, and we attempt to predict these suspicious circumstances and provide help in making the right decisions at the right time. Furthermore, most of the models that address crowd disasters belong to the physical or the cognitive approaches. They study pedestrian flow and collision avoidance, etc., and they use walking speed and angle of vision. However, in this work, based on a behavioral rules approach, we aim to model emergent emotion, behavior and influence in a crowd, taking into account particularly the personality of members of the crowd. For this purpose, we have combined the OCEAN Openness, Consciousness, Extraversion, Agreeableness, and Neuroticism personality model with the OCC Ortony, Clore, and Collins emotional model to indicate the susceptibility of each of the five personality factors to feeling every emotion. Then we proposed an approach that uses first fuzzy logic for the emotional modeling of critical emotions of members of the crowd at the announcement or the presence of unusual events, in order to quantify emotions. Then, we model the behavior and the tendency towards actions using probability theory. Finally, the influence among the members of the crowd is modeled using the neighborhood principle and cellular automata.