Efficient human detection in crowded environment

Multimedia Systems - Detecting humans in crowded environment is profitable but challenging in video surveillance. We propose an efficient human detection method by combining both motion and...     

Counting moving persons in crowded scenes

The paper presents a method for estimating the number of moving people in a scene for video surveillance applications. The method performance has been characterized on the public database used for the PETS 2009 and 2010 international competitions; the proposed method has been compared, on the same database, with the PETS competitions participants. The system exhibits a high accuracy, and revealed to be so fast that it can be used in real time surveillance applications. The rationale of the method lies on the extraction of suited scale-invariant feature points and the successive selection among them of the moving ones, under the hypothesis that the latter are associated to moving people. The perspective distortions are taken into account by dividing the input frames into smaller horizontal zones, each having (approximately) the same perspective effects. Therefore, the evaluation of the number of people is separately carried out for each zone, and the results are summed up. The most important peculiarity of the proposed method is the availability of a simple training procedure using a brief video sequence that shows a person walking around in the scene; the procedure automatically evaluates all the parameters needed by the system, thus making the method particularly suited for end-user applications.     

Anomaly detection using sparse reconstruction in crowded scenes

Multimedia Tools and Applications - In this paper, we propose an algorithm of anomaly detection in crowded scenes by using sparse representation over the normal bases. First, the histogram of...     

Segmentation and tracking of multiple humans in crowded environments

Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model based approach to interpret the image observations by multiple, partially occluded human hypotheses in a Bayesian framework. We define a joint image likelihood for multiple humans based on the appearance of the humans, the visibility of body obtained by occlusion reasoning, and foreground/background separation. The optimal solution is obtained by using an efficient sampling method, data-driven Markov chain Monte Carlo (DDMCMC), which uses image observations for proposal probabilities. Knowledge of various aspects including human shape, camera model, and image cues are integrated in one theoretically sound framework. We present experimental results and quantitative evaluation, demonstrating that the resulting approach is effective for very challenging data.     

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.     

Anomaly detection in crowded scenes using motion energy model

Multimedia Tools and Applications - We present a new method for detection of abnormal behaviors in crowded scenes. Based on statistics of low-level feature—optical flow, which describes human...     

Estimation of number of people in crowded scenes using perspectivetransformation

In the past, the estimation of crowd density has become an important topic in the field of automatic surveillance systems. In this paper, the developed system goes one step further to estimate the number of people in crowded scenes in a complex background by using a single image. Therefore, more valuable information than crowd density can be obtained. There are two major steps in this system: recognition of the head-like contour and estimation of crowd size. First, the Haar wavelet transform is used to extract the featured area of the head-like contour, and then the support vector machine is used to classify these featured area as the contour of a head or not. Next, the perspective transforming technique of computer vision is used to estimate crowd size more accurately. Finally, a model world is constructed to test this proposed system and the system is also applied for real-world images     

Hostile intent in public crowded spaces: A field study

Hostile reconnaissance is vital to successful terrorist activity. Individuals carrying out this activity are likely to experience raised levels of stress and this will manifest itself at biological, physiological, psychological and behavioural levels, providing an opportunity for detection. A field trial was undertaken in an ecologically valid environment measuring variables considered likely to be salient during hostile intent. The parameters examined in the field trial varied in a predictable manner and suggest that stressed individuals secrete a volatile steroid based marker that could form the basis for remote detection. Thus, overall the findings of this research provide a validated model of hostile intent that can be used by other researchers to test interventions aimed at detecting or deterring hostile intent.     

Scene Aware Detection and Block Assignment Tracking in crowded scenes

How far can human detection and tracking go in real world crowded scenes? Many algorithms often fail in such scenes due to frequent and severe occlusions as well as viewpoint changes. In order to handle these difficulties, we propose Scene Aware Detection (SAD) and Block Assignment Tracking (BAT) that incorporate with some available scene models (e.g. background, layout, ground plane and camera models). The SAD is proposed for accurate detection through utilizing 1) camera model to deal with viewpoint changes by rectifying sub-images, 2) a structural filter approach to handle occlusions based on a feature sharing mechanism in which a three-level hierarchical structure is built for humans, and 3) foregrounds for pruning negative and false positive samples and merging intermediate detection results. Many detection or appearance based tracking systems are prone to errors in occluded scenes because of failures of detectors and interactions of multiple objects. Differently, the BAT formulates tracking as a block assignment process, where blocks with the same label form the appearance of one object. In the BAT, we model objects on two levels, one is the ensemble level to measure how it is like an object by discriminative models, and the other one is the block level to measure how it is like a target object by appearance and motion models. The main advantage of BAT is that it can track an object even when all the part detectors fail as long as the object has assigned blocks. Extensive experiments in many challenging real world scenes demonstrate the efficiency and effectiveness of our approach.     

中文信息导航系统的关键软件技术分析

信息导航系统的设计与开发涉及广泛的技术问题。介绍了信息导航系统的特点和现状以及信息导行系统的总体设计,并主要就大规模中文信息导航系统设计中的关键软件技术进行了讨论,包括搜索引擎Robot、中文信息处理系统和导航系统相关服务的设计及其需要解决的主要技术问题,并提供了相应问题的解决方法。最后,简要提到了导航系统设计中需要解决的主要硬件技术问题。     

Object and patch based anomaly detection and localization in crowded scenes

Multimedia Tools and Applications - Detecting and localizing anomalies in crowded scenes is an ongoing challenge for public security. Existing approaches are mainly based on patches and...     

Abnormal event detection and localization in crowded scenes based on PCANet

Detecting and localizing abnormal events in crowded scenes still remains a challenging task among computer vision community. An unsupervised framework is proposed in this paper to address the problem. Low-level features and optical flows (OF) of video sequences are extracted to represent motion information in the temporal domain. Moreover, abnormal events usually occur in local regions and are closely linked to their surrounding areas in the spatial domain. To extract high-level information from local regions and model the relationship in spatial domain, the first step is to calculate optical flow maps and divide them into a set of non-overlapping sub-maps. Next, corresponding PCANet models are trained using the sub-maps at same spatial location in the optical flow maps. Based on the block-wise histograms extracted by PCANet models, a set of one-class classifiers are trained to predict the anomaly scores of test frames. The framework is completely unsupervised because it utilizes only normal videos. Experiments were carried out on UCSD Ped2 and UMN datasets, and the results show competitive performance of this framework when compared with other state-of-the-art methods.     

Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes

Tracking pedestrians is a vital component of many computer vision applications, including surveillance, scene understanding, and behavior analysis. Videos of crowded scenes present significant challenges to tracking due to the large number of pedestrians and the frequent partial occlusions that they produce. The movement of each pedestrian, however, contributes to the overall crowd motion (i.e., the collective motions of the scene's constituents over the entire video) that exhibits an underlying spatially and temporally varying structured pattern. In this paper, we present a novel Bayesian framework for tracking pedestrians in videos of crowded scenes using a space-time model of the crowd motion. We represent the crowd motion with a collection of hidden Markov models trained on local spatio-temporal motion patterns, i.e., the motion patterns exhibited by pedestrians as they move through local space-time regions of the video. Using this unique representation, we predict the next local spatio-temporal motion pattern a tracked pedestrian will exhibit based on the observed frames of the video. We then use this prediction as a prior for tracking the movement of an individual in videos of extremely crowded scenes. We show that our approach of leveraging the crowd motion enables tracking in videos of complex scenes that present unique difficulty to other approaches.     

A hybrid tracking method for scaled and oriented objects in crowded scenes

Traditional kernel based means shift assumes constancy of the object scale and orientation during the course of tracking and uses a symmetric/asymmetric kernel, such as a circle or an ellipse for target representation. In a tracking scenario, it is not uncommon to observe objects with complex shapes whose scale and orientation constantly change due to the camera and object motions. In this paper, we propose a multi object tracking method which tracks the complete object regions, adapts to changing scale and orientation, and assigns consistent labels to each object throughout real world video sequences. Our approach has five major components: (1) dynamic background subtraction, (2) level sets, (3) mean shift convergence, (4) object identification, and (5) occlusion handling. The experimental results show that the proposed method is superior to the traditional mean shift tracking in the following aspects: (1) it provides consistent multi objects tracking instead of single object throughout the video, (2) it is not affected by the scale and orientation changes of the tracked objects, (3) its computational complexity is much less than traditional mean shift due to using level set method instead of probability density.