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Development of smart cities has grasped much attention in research community and industry as well. Smart healthcare, communication, infrastructure are required for the development of smart cities. Security is one of the major concern in the development of smart cities. Automatic surveillance helps in boosting security in multiple areas like traffic, hospitals, schools, and industries etc. Video camera...
Human machine interaction becomes one of the most research topics in multimedia processing, traditional techniques for communication are developed in order to tackle technology advances and allow disable person to communicate easily with the machine, and to understand their activity using computer computing. In this paper we are focused on human behavior analysis from video scene and it is worth noticed...
We present a novel video representation for human action recognition by considering temporal sequences of visual words. Based on state-of-the-art dense trajectories, we introduce temporal bundles of dominant, that is most frequent, visual words. These are employed to construct a complementary action representation of ordered dominant visual word sequences, that additionally incorporates fine grained...
Sign language recognition has been the focus of research in recent years because it has enabled the use of sign languages, which are the main medium of communication for the hearing impaired, for human-computer interaction. In this work, we propose a method to recognize signs using Improved Dense Trajectory (IDT) features which were previously used in large-scale action recognition. Fisher Vectors...
Recently CCTV-based behavior recognition have gained considerable attention in the transportation surveillance systems to identify normalities, such as traffic jams, accidents, and dangerous driving. An improved method is presented in this paper for the traffic behavior surveillance system by discovering more highly specific features based on the trajectory information. The multiple sparse feature...
Abnormal activity detection plays an important role in many areas such as surveillance, military installations, and sports. Existing abnormal activity detectors mostly rely on motion data obtained over a number of frames to characterize abnormality. However, only motion may not be able to capture all forms of abnormality, in particular, poses that do not amount to motion "outliers". In this...
Human action recognition from video input has seen much interest over the last decade. In recent years, the trend is clearly towards action recognition in real-world, unconstrained conditions (i.e. not acted) with an ever growing number of action classes. Much of the work so far has used single frames or sequences of frames where each frame was treated individually. This paper investigates the contribution...
While some indoor Location Based Services (LBSs), such as medical equipment location in hospitals or people location in museums, do not need to estimate the trajectory of devices at short time intervals, some others, such as people guidance, require a frequent estimation of the device position. When providing an LBS for the latter, motion models and the information provided from motion sensors are...
Smartphone based indoor localization caught massive interest of the localization community in recent years. Combining pedestrian dead reckoning obtained using the phone's inertial sensors with the Graph SLAM (Simultaneous Localization and Mapping) algorithm is one of the most effective approaches to reconstruct the entire pedestrian trajectory given a set of visited landmarks during movement. A key...
Predicting driver behavior is a key component for Advanced Driver Assistance Systems (ADAS). In this paper, a novel approach based on Support Vector Machine and Bayesian filtering is proposed for online lane change intention prediction. The approach uses the multiclass probabilistic outputs of the Support Vector Machine as an input to the Bayesian filter, and the output of the Bayesian filter is used...
This paper presents a novel method to recognize the human gesture using binary decision tree and Multi-class Support Vector Machine (MCSVM). In a learning stage, 3D trajectory of the human gesture by a kinect sensor is assigned into the tree node of the binary decision tree according to its distribution property. The user's gesture trajectory is resampled and normalized, and we extract the chain code...
Since isolated letter handwriting recognition is an essential step for online hand writing recognition, we present in this paper an efficient and writer independent isolated letter handwriting recognition system using pen trajectory modeling for feature extraction and a multi-stage Support Vector Machines (SVM) for classification. Inheriting the good discriminating ability of SVM while modeling sequential...
We investigate the application of structured output learning (SOL) in automatic annotation of court games. We formulate the problem of event classification in court games as one of learning a mapping from features to structured labels, and employ structured SVM to achieve a max-margin solution. We compare closely the more popular generative approach based on the hidden Markov model (HMM) with our...
The purpose of this work is to investigate the severity characteristics of abnormal events at intersections by using video processing techniques and statistical deviation analysis methods. In order to detect the abnormal events, trajectory of normal vehicle motions are clustered and common route models are learned by Continuous Hidden Markov Model. In the second part, the abnormal spatio-temporal...
This study describes a new approach for pedestrian behaviour analysis in simulated urban environments. A software system was developed to analyse the dynamics of pedestrians with a focus on their movement trajectories and the angle between the pedestrian's movement vector and their gaze vector. One-class support vector machines and dynamic time warping were applied for outlier detection in order to...
A real-time hand gesture recognition system is developed for human-robot interaction of service robot. The proposed system is mainly composed of two subsystems: one for gesture recognition, and the other for the classification of the gesture motion. The system first uses a cascade classifier to locate the potential hand region from video frame. Then, Gabor wavelets transformation is applied to extract...
Many event analysis systems are based on the detection of uncommon feature patterns that could be associated to anomalous events; the uncommon patterns are identified by comparison with a "normality model" describing the previously acquired data. In this work we propose an anomaly detection system based on trajectory clustering with single-class support vector machines. However, SVM parameter...
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