scholarly journals Crowd behavior analysis using MoDTA approach

2019 ◽  
Vol 15 (1) ◽  
pp. 484
Author(s):  
Savitha C ◽  
Dr. Ramesh. D
Keyword(s):  
System Performance ◽  
Prior Information ◽  
Motion Model ◽  
Crowd Behavior ◽  
Significant Extent ◽  
Computational Costs ◽  
Crowd Behavior Analysis ◽  
Detection And Tracking ◽  
Crowded Scenes ◽  
Surveillance Applications

<span>In order to analyze the behaviors of human, significant extent of work has been carried out in the video surveillance applications. While considering the crowded scenes, the adopted features are crafted manually which have a great side to detect anomaly. It requires prior information and is hard to extract from complex video scenes and also it involves huge computational costs. In this paper, we are proposing multi-observational detection and tracking approach (MoDTA) that is based on observational filter. The MoDTA initially acquires<span>  </span>people location in an image, </span>so that is <span>can detect conviction value at pointed locations which generally increases with respect to people density. In the phase of tracking, MoDTA computes the multiple observed weight values and individual features, also advection particle is used at motion model in order to facilitate the dense scenario tracking. Coefficient of correlation is used as template detector and the function of template detector is to estimate the upcoming object. Our proposed MoDTA is compared with other existing detection and tracking methods in order to evaluate the system performance.<span>  </span></span>

scholarly journals Robust pedestrian detection and tracking in crowded scenes

2009 ◽  
Vol 27 (10) ◽  
pp. 1445-1458 ◽  
Author(s):  
Philip Kelly ◽  
Noel E. O’Connor ◽  
Alan F. Smeaton
Keyword(s):  
Pedestrian Detection ◽  
Detection And Tracking ◽  
Crowded Scenes

scholarly journals On the Method of Determining Onboard Ephemeris Data L1OC and L3OC GLONASS Aimed at Testing Positioning Algorithms in the GNSS Receiver

2020 ◽  
pp. 1-15
Author(s):  
Taliya G. Sharfunova ◽  
Daria A. Krasilnikova
Keyword(s):  
Prior Information ◽  
Least Squares Method ◽  
Initial Conditions ◽  
Model Parameters ◽  
Motion Model ◽  
Digital Form ◽  
Tikhonov’S Regularization ◽  
Navigation Equipment ◽  
Spacecraft Position

The paper considers a method of determining GLONASS ephemeris data in the L1OC and L3OC digital form aimed at testing the algorithms of accurate navigation determinations in consumer navigation equipment. The task of determining long-term motion model parameters of a navigation spacecraft is set as nonlinear problem of designing a matching model. This task is unstable and according to the analysis is categorized as incorrect. The application of a traditional least squares method to determine the long-term motion model parameters of a navigation spacecraft does not allow to obtain equally accurate solutions of condition equations system when initial conditions and/ or iterations amount have been changed. In this respect, Tikhonov’s regularization method has been carried out. It is based on the application of additional prior information. The obtained results have been tested by the comparison of estimated navigation spacecraft position according to the adjusted (speed and acceleration) ephemeris data, long-term motion model parameters and SP3 final reference ephemeris published on the SVOEVP website. The long-term motion model parameters of GLONASS navigation spacecraft that were defined by regularizing algorithms, have allowed to calculate the position of navigation spacecraft orbital grouping in terms of four-hours fitting intervals within 0,2 m tolerance (maximum deviations according to module of estimated navigation spacecraft position from SVOEVP SP3 reference ephemeris)

scholarly journals Modeling Pedestrian Motion in Crowded Scenes Based on the Shortest Path Principle

2021 ◽  
Vol 12 (1) ◽  
pp. 381
Author(s):  
Yi Zou ◽  
Yuncai Liu
Keyword(s):  
Shortest Path ◽  
State Of The Art ◽  
Meaningful Work ◽  
Movement Behavior ◽  
Motion Model ◽  
Human Dynamics ◽  
Motion Trajectories ◽  
Crowd Motion ◽  
Crowded Scenes ◽  
Part Motion

In the computer vision field, understanding human dynamics is not only a great challenge but also very meaningful work, which plays an indispensable role in public safety. Despite the complexity of human dynamics, physicists have found that pedestrian motion in a crowd is governed by some internal rules, which can be formulated as a motion model, and an effective model is of great importance for understanding and reconstructing human dynamics in various scenes. In this paper, we revisit the related research in social psychology and propose a two-part motion model based on the shortest path principle. One part of the model seeks the origin and destination of a pedestrian, and the other part generates the movement path of the pedestrian. With the proposed motion model, we simulated the movement behavior of pedestrians and classified them into various patterns. We next reconstructed the crowd motions in a real-world scene. In addition, to evaluate the effectiveness of the model in crowd motion simulations, we created a new indicator to quantitatively measure the correlation between two groups of crowd motion trajectories. The experimental results show that our motion model outperformed the state-of-the-art model in the above applications.

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