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This study proposes a multi-level trajectory analysis method for modeling traffic behavior from an ego-centric view, where on-road vehicle trajectories are collected based on the authors' previous studies of an on-board system consisting of multiple 2D lidar sensors. From an input set of trajectories, a set of hot regions (topics) that trajectory points most frequently present are first discovered...
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 new approach to extracting low-dimensional neural trajectories that summarize the electrocorticographic (ECoG) signals recorded with high-channel-count electrode arrays implanted subdurally. In our approach, Hidden-Markov Factor Analysis (HMFA), a finite set of factor analyzers are used to model the relationship between the high-dimensional ECoG neural space and a low-dimensional latent...
In this paper we have briefly reviewed the Statistical Parametric Speech Synthesis (SPSS ), based on hidden Markov model. The non-mathematical introduction of SPSS have been introduced. Have emphasized the recent emerging techniques used in SPSS like Autoregressive HMM model, Gaussian Process Regression(GPR), Neural Autoregressive Distribution Estimators (NADE) overcoming Restricted Boltzmann Machines...
Today, gesture analysis lacks of global models able to characterize motion expressivity and its communicational character. In this paper, we propose a set of new gesture descriptors inspired from Laban Movement Analysis (LMA) and based on 3D body trajectories. We test our descriptors ability to characterize human actions in a machine learning framework (with SVM and different random forest techniques)...
This paper proposes a new approach to describe traffic scene including vehicle collisions and vehicle anomalies at intersections by video processing and motion statistic techniques. The research mainly targets on extracting abnormal event characteristics at intersections and learning normal traffic flow by trajectory clustering techniques. Detecting and analyzing accident events are done by observing...
This paper presents a novel approach to describe traffic accident events at intersections in human-understandable way using automated video processing techniques. The research mainly proposes a new technique for video-based traffic accident analysis by extracting abnormal event characteristics at intersections. The approach relies on learning normal traffic flow using trajectory clustering techniques,...
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...
In the field of video surveillance, adaptive Gaussian mixture model (GMM) is widely used as the background-pixel dynamic modeling approach. GMM produced each pixel Gaussian distribution corresponds to the respective, but this ignores the impact of the movement of the object itself. The ideas of object kinematic model is presented to guide the number of distribution in the process of iterative, which...
People social interaction analysis is a complex and interesting problem that can be faced from several points of view depending on the application context. In videosurveillance contexts many indicators of people habits and relations exist and, among these, people trajectories analysis can reveal many aspects of the way people behave in social environments. We propose a statistical framework for trajectories...
This work introduces a new approach to modeling object trajectories in image sequences. Trajectories performed by natural objects (e.g., people, animals) typically depend on the position of each object in the scene and can change in an unpredictable way. Despite this diversity, there is often a small number of typical motion patterns based on which it is possible to explain all the observed trajectories...
Recently human gait has been considered as a useful biometric supporting high performance human identification systems. Here we are more interested in understanding the gait including the direction rather than the human identity. We propose the use of SOM for interpreting human gait activity using the silhouettes of a pedestrian. The technique and the result may be able to find applications in view-independent...
Motion analysis is a very attractive research direction in computer vision field. In this paper, we propose a framework for analyzing real vehicle motion in visual traffic surveillance by using Segment Model (SM), which is a kind of probabilistic model. SM can grasp the underlying information of observation sequence by using segment distribution. It has been proved to be more precise than that of...
With the continuous improvements in computer-vision techniques, automatic low-cost video surveillance gradually emerges for consumer applications. Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities in video sequences. We propose a fast analyzer for surveillance video, which aims at automatic analysis of human behavior and semantic events....
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