Automatic human behaviour anomaly detection in surveillance video

This thesis work focusses upon developing the capability to automatically evaluate and detect anomalies in human behaviour from surveillance video. We work with static monocular cameras in crowded urban surveillance scenarios, particularly air- ports and commercial shopping areas. Typically a person is 100 to 200 pixels high in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo- ple at any given time. Our procedure evaluates human behaviour unobtrusively to determine outlying behavioural events, agging abnormal events to the operator. In order to achieve automatic human behaviour anomaly detection we address the challenge of interpreting behaviour within the context of the social and physical environment. We develop and evaluate a process for measuring social connectivity between individuals in a scene using motion and visual attention features. To do this we use mutual information and Euclidean distance to build a social similarity matrix which encodes the social connection strength between any two individuals. We de- velop a second contextual basis which acts by segmenting a surveillance environment into behaviourally homogeneous subregions which represent high tra c slow regions and queuing areas. We model the heterogeneous scene in homogeneous subgroups using both contextual elements. We bring the social contextual information, the scene context, the motion, and visual attention features together to demonstrate a novel human behaviour anomaly detection process which nds outlier behaviour from a short sequence of video. The method, Nearest Neighbour Ranked Outlier Clusters (NN-RCO), is based upon modelling behaviour as a time independent se- quence of behaviour events, can be trained in advance or set upon a single sequence. We nd that in a crowded scene the application of Mutual Information-based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in all the datasets we test upon. We additionally demonstrate that our work is applicable to other data domains. We demonstrate upon the Automatic Identi cation Signal data in the maritime domain. Our work is capable of identifying abnormal shipping behaviour using joint motion dependency as analogous for social connectivity, and similarly segmenting the shipping environment into homogeneous regions

Taming Crowded Visual Scenes

Computer vision algorithms have played a pivotal role in commercial video surveillance systems for a number of years. However, a common weakness among these systems is their inability to handle crowded scenes. In this thesis, we have developed algorithms that overcome some of the challenges encountered in videos of crowded environments such as sporting events, religious festivals, parades, concerts, train stations, airports, and malls. We adopt a top-down approach by first performing a global-level analysis that locates dynamically distinct crowd regions within the video. This knowledge is then employed in the detection of abnormal behaviors and tracking of individual targets within crowds. In addition, the thesis explores the utility of contextual information necessary for persistent tracking and re-acquisition of objects in crowded scenes. For the global-level analysis, a framework based on Lagrangian Particle Dynamics is proposed to segment the scene into dynamically distinct crowd regions or groupings. For this purpose, the spatial extent of the video is treated as a phase space of a time-dependent dynamical system in which transport from one region of the phase space to another is controlled by the optical flow. Next, a grid of particles is advected forward in time through the phase space using a numerical integration to generate a flow map . The flow map relates the initial positions of particles to their final positions. The spatial gradients of the flow map are used to compute a Cauchy Green Deformation tensor that quantifies the amount by which the neighboring particles diverge over the length of the integration. The maximum eigenvalue of the tensor is used to construct a forward Finite Time Lyapunov Exponent (FTLE) field that reveals the Attracting Lagrangian Coherent Structures (LCS). The same process is repeated by advecting the particles backward in time to obtain a backward FTLE field that reveals the repelling LCS. The attracting and repelling LCS are the time dependent invariant manifolds of the phase space and correspond to the boundaries between dynamically distinct crowd flows. The forward and backward FTLE fields are combined to obtain one scalar field that is segmented using a watershed segmentation algorithm to obtain the labeling of distinct crowd-flow segments. Next, abnormal behaviors within the crowd are localized by detecting changes in the number of crowd-flow segments over time. Next, the global-level knowledge of the scene generated by the crowd-flow segmentation is used as an auxiliary source of information for tracking an individual target within a crowd. This is achieved by developing a scene structure-based force model. This force model captures the notion that an individual, when moving in a particular scene, is subjected to global and local forces that are functions of the layout of that scene and the locomotive behavior of other individuals in his or her vicinity. The key ingredients of the force model are three floor fields that are inspired by research in the field of evacuation dynamics; namely, Static Floor Field (SFF), Dynamic Floor Field (DFF), and Boundary Floor Field (BFF). These fields determine the probability of moving from one location to the next by converting the long-range forces into local forces. The SFF specifies regions of the scene that are attractive in nature, such as an exit location. The DFF, which is based on the idea of active walker models, corresponds to the virtual traces created by the movements of nearby individuals in the scene. The BFF specifies influences exhibited by the barriers within the scene, such as walls and no-entry areas. By combining influence from all three fields with the available appearance information, we are able to track individuals in high-density crowds. The results are reported on real-world sequences of marathons and railway stations that contain thousands of people. A comparative analysis with respect to an appearance-based mean shift tracker is also conducted by generating the ground truth. The result of this analysis demonstrates the benefit of using floor fields in crowded scenes. The occurrence of occlusion is very frequent in crowded scenes due to a high number of interacting objects. To overcome this challenge, we propose an algorithm that has been developed to augment a generic tracking algorithm to perform persistent tracking in crowded environments. The algorithm exploits the contextual knowledge, which is divided into two categories consisting of motion context (MC) and appearance context (AC). The MC is a collection of trajectories that are representative of the motion of the occluded or unobserved object. These trajectories belong to other moving individuals in a given environment. The MC is constructed using a clustering scheme based on the Lyapunov Characteristic Exponent (LCE), which measures the mean exponential rate of convergence or divergence of the nearby trajectories in a given state space. Next, the MC is used to predict the location of the occluded or unobserved object in a regression framework. It is important to note that the LCE is used for measuring divergence between a pair of particles while the FTLE field is obtained by computing the LCE for a grid of particles. The appearance context (AC) of a target object consists of its own appearance history and appearance information of the other objects that are occluded. The intent is to make the appearance descriptor of the target object more discriminative with respect to other unobserved objects, thereby reducing the possible confusion between the unobserved objects upon re-acquisition. This is achieved by learning the distribution of the intra-class variation of each occluded object using all of its previous observations. In addition, a distribution of inter-class variation for each target-unobservable object pair is constructed. Finally, the re-acquisition decision is made using both the MC and the AC

Interactive Tracking, Prediction, and Behavior Learning of Pedestrians in Dense Crowds

The ability to automatically recognize human motions and behaviors is a key skill for autonomous machines to exhibit to interact intelligently with a human-inhabited environment. The capabilities autonomous machines should have include computing the motion trajectory of each pedestrian in a crowd, predicting his or her position in the near future, and analyzing the personality characteristics of the pedestrian. Such techniques are frequently used for collision-free robot navigation, data-driven crowd simulation, and crowd surveillance applications. However, prior methods for these problems have been restricted to low-density or sparse crowds where the pedestrian movement is modeled using simple motion models. In this thesis, we present several interactive algorithms to extract pedestrian trajectories from videos in dense crowds. Our approach combines different pedestrian motion models with particle tracking and mixture models and can obtain an average of $20\%$ improvement in accuracy in medium-density crowds over prior work. We compute the pedestrian dynamics from these trajectories using Bayesian learning techniques and combine them with global methods for long-term pedestrian prediction in densely crowded settings. Finally, we combine these techniques with Personality Trait Theory to automatically classify the dynamic behavior or the personality of a pedestrian based on his or her movements in a crowded scene. The resulting algorithms are robust and can handle sparse and noisy motion trajectories. We demonstrate the benefits of our long-term prediction and behavior classification methods in dense crowds and highlight the benefits over prior techniques. We highlight the performance of our novel algorithms on three different applications. The first application is interactive data-driven crowd simulation, which includes crowd replication as well as the combination of pedestrian behaviors from different videos. Secondly, we combine the prediction scheme with proxemic characteristics from psychology and use them to perform socially-aware navigation. Finally, we present novel techniques for anomaly detection in low-to medium-density crowd videos using trajectory-level behavior learning.Doctor of Philosoph

Visual Analysis of Videos of Crowded Scenes

Automatic, vision-based analysis of crowds has implications in a number of fields, but faces unique challenges due to the large number of pedestrians within the scenes. The movement of each pedestrian contributes to the overall crowd motion (i.e., the collective motions of the scene's constituents) that varies spatially across the frame and temporally over the video. This thesis explores how to model the dynamically varying crowd motion, and how to leverage it to perform vision-based analysis on videos of crowded scenes. The crowd motion serves as a scene-centric constraint (i.e., representing the motion in the entire video), compared with conventional objectcentric methods that build on individual constituents. By exploring what information the crowd motion can represent, we demonstrate the impact of leveraging our model on three problems facing video analysis of crowded scenes. First, we represent the crowd motion using a novel statistical model of local motion patterns (i.e., the motion in local space-time areas). By doing so, we may learn the spatially and temporally varying underlying structure of the crowd motion from an example video of crowd behavior. Second, we use our model to represent the typical crowd activity (i.e., the crowd's steady-state) and detect unusual events in local areas of the video. Specifically, we identify local motion patterns that statistically deviate from our learned model. Our space-time model enables detection and isolation of unusual events that are specific to the scene and the location within the video. Next, we use the crowd motion as an indicator of an individual's motion to perform tracking. Specifically, we predict the local motion patterns at different space-time locations of the video and use them as a prior to track individuals in a Bayesian framework. Leveraging the crowd motion provides an accurate prior that dynamically adapts to the space-time variations of the crowd. Finally, we explore how to measure how much individual pedestrians conform to the movement of the crowd. To achieve this, we use our crowd model to indicate the future locations of pedestrians, and compare the direction they would move to their instantaneous optical flow. By identifying deviations from the crowd, we identify global unusual events and augment our tracking method to model the individuality of each target. We compare with conventional object-centric methods and those that do not encode the space-time varying motion of the crowd. We demonstrate that our scene-centric approach (i.e, one that starts with the crowd motion) advances video analysis closer to the robustness and dependability needed for real-world video analysis of scenes containing a large number of pedestrians.Ph.D., Computer Science -- Drexel University, 201

Robust Subspace Estimation Using Low-rank Optimization. Theory And Applications In Scene Reconstruction, Video Denoising, And Activity Recognition.

In this dissertation, we discuss the problem of robust linear subspace estimation using low-rank optimization and propose three formulations of it. We demonstrate how these formulations can be used to solve fundamental computer vision problems, and provide superior performance in terms of accuracy and running time. Consider a set of observations extracted from images (such as pixel gray values, local features, trajectories . . . etc). If the assumption that these observations are drawn from a liner subspace (or can be linearly approximated) is valid, then the goal is to represent each observation as a linear combination of a compact basis, while maintaining a minimal reconstruction error. One of the earliest, yet most popular, approaches to achieve that is Principal Component Analysis (PCA). However, PCA can only handle Gaussian noise, and thus suffers when the observations are contaminated with gross and sparse outliers. To this end, in this dissertation, we focus on estimating the subspace robustly using low-rank optimization, where the sparse outliers are detected and separated through the `1 norm. The robust estimation has a two-fold advantage: First, the obtained basis better represents the actual subspace because it does not include contributions from the outliers. Second, the detected outliers are often of a specific interest in many applications, as we will show throughout this thesis. We demonstrate four different formulations and applications for low-rank optimization. First, we consider the problem of reconstructing an underwater sequence by removing the iii turbulence caused by the water waves. The main drawback of most previous attempts to tackle this problem is that they heavily depend on modelling the waves, which in fact is ill-posed since the actual behavior of the waves along with the imaging process are complicated and include several noise components; therefore, their results are not satisfactory. In contrast, we propose a novel approach which outperforms the state-of-the-art. The intuition behind our method is that in a sequence where the water is static, the frames would be linearly correlated. Therefore, in the presence of water waves, we may consider the frames as noisy observations drawn from a the subspace of linearly correlated frames. However, the noise introduced by the water waves is not sparse, and thus cannot directly be detected using low-rank optimization. Therefore, we propose a data-driven two-stage approach, where the first stage “sparsifies” the noise, and the second stage detects it. The first stage leverages the temporal mean of the sequence to overcome the structured turbulence of the waves through an iterative registration algorithm. The result of the first stage is a high quality mean and a better structured sequence; however, the sequence still contains unstructured sparse noise. Thus, we employ a second stage at which we extract the sparse errors from the sequence through rank minimization. Our method converges faster, and drastically outperforms state of the art on all testing sequences. Secondly, we consider a closely related situation where an independently moving object is also present in the turbulent video. More precisely, we consider video sequences acquired in a desert battlefields, where atmospheric turbulence is typically present, in addition to independently moving targets. Typical approaches for turbulence mitigation follow averaging or de-warping techniques. Although these methods can reduce the turbulence, they distort the independently moving objects which can often be of great interest. Therefore, we address the iv problem of simultaneous turbulence mitigation and moving object detection. We propose a novel three-term low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the background, the turbulence, and the object. We simplify this extremely difficult problem into a minimization of nuclear norm, Frobenius norm, and `1 norm. Our method is based on two observations: First, the turbulence causes dense and Gaussian noise, and therefore can be captured by Frobenius norm, while the moving objects are sparse and thus can be captured by `1 norm. Second, since the object’s motion is linear and intrinsically different than the Gaussian-like turbulence, a Gaussian-based turbulence model can be employed to enforce an additional constraint on the search space of the minimization. We demonstrate the robustness of our approach on challenging sequences which are significantly distorted with atmospheric turbulence and include extremely tiny moving objects. In addition to robustly detecting the subspace of the frames of a sequence, we consider using trajectories as observations in the low-rank optimization framework. In particular, in videos acquired by moving cameras, we track all the pixels in the video and use that to estimate the camera motion subspace. This is particularly useful in activity recognition, which typically requires standard preprocessing steps such as motion compensation, moving object detection, and object tracking. The errors from the motion compensation step propagate to the object detection stage, resulting in miss-detections, which further complicates the tracking stage, resulting in cluttered and incorrect tracks. In contrast, we propose a novel approach which does not follow the standard steps, and accordingly avoids the aforementioned diffi- culties. Our approach is based on Lagrangian particle trajectories which are a set of dense trajectories obtained by advecting optical flow over time, thus capturing the ensemble motions v of a scene. This is done in frames of unaligned video, and no object detection is required. In order to handle the moving camera, we decompose the trajectories into their camera-induced and object-induced components. Having obtained the relevant object motion trajectories, we compute a compact set of chaotic invariant features, which captures the characteristics of the trajectories. Consequently, a SVM is employed to learn and recognize the human actions using the computed motion features. We performed intensive experiments on multiple benchmark datasets, and obtained promising results. Finally, we consider a more challenging problem referred to as complex event recognition, where the activities of interest are complex and unconstrained. This problem typically pose significant challenges because it involves videos of highly variable content, noise, length, frame size . . . etc. In this extremely challenging task, high-level features have recently shown a promising direction as in [53, 129], where core low-level events referred to as concepts are annotated and modelled using a portion of the training data, then each event is described using its content of these concepts. However, because of the complex nature of the videos, both the concept models and the corresponding high-level features are significantly noisy. In order to address this problem, we propose a novel low-rank formulation, which combines the precisely annotated videos used to train the concepts, with the rich high-level features. Our approach finds a new representation for each event, which is not only low-rank, but also constrained to adhere to the concept annotation, thus suppressing the noise, and maintaining a consistent occurrence of the concepts in each event. Extensive experiments on large scale real world dataset TRECVID Multimedia Event Detection 2011 and 2012 demonstrate that our approach consistently improves the discriminativity of the high-level features by a significant margin

Human robot interaction in a crowded environment

Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3]. Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from people‟s movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robot‟s interaction, it may approach the person. Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]

Trending Paths: A New Semantic-level Metric for Comparing Simulated and Real Crowd Data

We propose a new semantic-level crowd evaluation metric in this paper. Crowd simulation has been an active and important area for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose the first approach based on finding semantic information represented by latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd behavior. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process (SV-DHDP) model. The fidelity of the patterns is computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly. Detailed evaluations and comparisons with existing metrics show that our method is a good alternative for comparing crowd data at a different level and also works with more types of data, holds fewer assumptions and is more robust to noise

People detection and tracking in crowded scenes

People are often a central element of visual scenes, particularly in real-world street scenes. Thus it has been a long-standing goal in Computer Vision to develop methods aiming at analyzing humans in visual data. Due to the complexity of real-world scenes, visual understanding of people remains challenging for machine perception. In this thesis we focus on advancing the techniques for people detection and tracking in crowded street scenes. We also propose new models for human pose estimation and motion segmentation in realistic images and videos. First, we propose detection models that are jointly trained to detect single person as well as pairs of people under varying degrees of occlusion. The learning algorithm of our joint detector facilitates a tight integration of tracking and detection, because it is designed to address common failure cases during tracking due to long-term inter-object occlusions. Second, we propose novel multi person tracking models that formulate tracking as a graph partitioning problem. Our models jointly cluster detection hypotheses in space and time, eliminating the need for a heuristic non-maximum suppression. Furthermore, for crowded scenes, our tracking model encodes long-range person re-identification information into the detection clustering process in a unified and rigorous manner. Third, we explore the visual tracking task in different granularity. We present a tracking model that simultaneously clusters object bounding boxes and pixel level trajectories over time. This approach provides a rich understanding of the motion of objects in the scene. Last, we extend our tracking model for the multi person pose estimation task. We introduce a joint subset partitioning and labelling model where we simultaneously estimate the poses of all the people in the scene. In summary, this thesis addresses a number of diverse tasks that aim to enable vision systems to analyze people in realistic images and videos. In particular, the thesis proposes several novel ideas and rigorous mathematical formulations, pushes the boundary of state-of-the-arts and results in superior performance.Personen sind oft ein zentraler Bestandteil visueller Szenen, besonders in natürlichen Straßenszenen. Daher ist es seit langem ein Ziel der Computer Vision, Methoden zu entwickeln, um Personen in einer Szene zu analysieren. Aufgrund der Komplexität natürlicher Szenen bleibt das visuelle Verständnis von Personen eine Herausforderung für die maschinelle Wahrnehmung. Im Zentrum dieser Arbeit steht die Weiterentwicklung von Verfahren zur Detektion und zum Tracking von Personen in Straßenszenen mit Menschenmengen. Wir erforschen darüber hinaus neue Methoden zur menschlichen Posenschätzung und Bewegungssegmentierung in realistischen Bildern und Videos. Zunächst schlagen wir Detektionsmodelle vor, die gemeinsam trainiert werden, um sowohl einzelne Personen als auch Personenpaare bei verschiedener Verdeckung zu detektieren. Der Lernalgorithmus unseres gemeinsamen Detektors erleichtert eine enge Integration von Tracking und Detektion, da er darauf konzipiert ist, häufige Fehlerfälle aufgrund langfristiger Verdeckungen zwischen Objekten während des Tracking anzugehen. Zweitens schlagen wir neue Modelle für das Tracking mehrerer Personen vor, die das Tracking als Problem der Graphenpartitionierung formulieren. Unsere Mod- elle clustern Detektionshypothesen gemeinsam in Raum und Zeit und eliminieren dadurch die Notwendigkeit einer heuristischen Unterdrückung nicht maximaler De- tektionen. Bei Szenen mit Menschenmengen kodiert unser Trackingmodell darüber hinaus einheitlich und genau Informationen zur langfristigen Re-Identifizierung in den Clusteringprozess der Detektionen. Drittens untersuchen wir die visuelle Trackingaufgabe bei verschiedener Gran- ularität. Wir stellen ein Trackingmodell vor, das im Zeitablauf gleichzeitig Begren- zungsrahmen von Objekten und Trajektorien auf Pixelebene clustert. Diese Herange- hensweise ermöglicht ein umfassendes Verständnis der Bewegung der Objekte in der Szene. Schließlich erweitern wir unser Trackingmodell für die Posenschätzung mehrerer Personen. Wir führen ein Modell zur gemeinsamen Graphzerlegung und Knoten- klassifikation ein, mit dem wir gleichzeitig die Posen aller Personen in der Szene schätzen. Zusammengefasst widmet sich diese Arbeit einer Reihe verschiedener Aufgaben mit dem gemeinsamen Ziel, Bildverarbeitungssystemen die Analyse von Personen in realistischen Bildern und Videos zu ermöglichen. Insbesondere schlägt die Arbeit mehrere neue Ansätze und genaue mathematische Formulierungen vor, und sie zeigt Methoden, welche die Grenze des neuesten Stands der Technik überschreiten und eine höhere Leistung von Bildverarbeitungssystemen ermöglichen