Determination of Object Directions Using Optical Flow for Crowd Monitoring

2013 ◽  
pp. 613-622 ◽  
Author(s):  
Aravinda S. Rao ◽  
Jayavardhana Gubbi ◽  
Slaven Marusic ◽  
Andrew Maher ◽  
Marimuthu Palaniswami
Keyword(s):  
Optical Flow ◽  
Crowd Monitoring

Development of an optical flow-through biosensor for the determination of sulphite in environmental samples

1995 ◽  
Vol 311 (3) ◽  
pp. 281-287 ◽  
Author(s):  
M.D.Luque de Castro ◽  
J.M. Fernández-Romero
Keyword(s):  
Optical Flow ◽  
Environmental Samples ◽  
Flow Through

scholarly journals Background Invariant Faster Motion Modeling for Drone Action Recognition

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 87
Author(s):  
Ketan Kotecha ◽  
Deepak Garg ◽  
Balmukund Mishra ◽  
Pratik Narang ◽  
Vipual Kumar Mishra
Keyword(s):  
Optical Flow ◽  
Action Recognition ◽  
Critical Issue ◽  
Human Action ◽  
Motion Modeling ◽  
Aerial Surveillance ◽  
Public And Private ◽  
Motion Feature ◽  
Crowd Monitoring ◽  
Top View

Visual data collected from drones has opened a new direction for surveillance applications and has recently attracted considerable attention among computer vision researchers. Due to the availability and increasing use of the drone for both public and private sectors, it is a critical futuristic technology to solve multiple surveillance problems in remote areas. One of the fundamental challenges in recognizing crowd monitoring videos’ human action is the precise modeling of an individual’s motion feature. Most state-of-the-art methods heavily rely on optical flow for motion modeling and representation, and motion modeling through optical flow is a time-consuming process. This article underlines this issue and provides a novel architecture that eliminates the dependency on optical flow. The proposed architecture uses two sub-modules, FMFM (faster motion feature modeling) and AAR (accurate action recognition), to accurately classify the aerial surveillance action. Another critical issue in aerial surveillance is a deficiency of the dataset. Out of few datasets proposed recently, most of them have multiple humans performing different actions in the same scene, such as a crowd monitoring video, and hence not suitable for directly applying to the training of action recognition models. Given this, we have proposed a novel dataset captured from top view aerial surveillance that has a good variety in terms of actors, daytime, and environment. The proposed architecture has shown the capability to be applied in different terrain as it removes the background before using the action recognition model. The proposed architecture is validated through the experiment with varying investigation levels and achieves a remarkable performance of 0.90 validation accuracy in aerial action recognition.

Determination of Enzymatic Activities Based on an Optical Flow-Through p-Nitrophenol Sensor

1993 ◽  
Vol 26 (9) ◽  
pp. 1847-1866 ◽  
Author(s):  
J. M. Fernández-Romero ◽  
M. D. Luque de Castro ◽  
M. Valcárcel
Keyword(s):  
Optical Flow ◽  
Enzymatic Activities ◽  
Flow Through

Characterisation of a gas-diffusion membrane-based optical flow-through sensor exemplified by the determination of nitrite

Talanta ◽  
2004 ◽  
Vol 64 (2) ◽  
pp. 278-282 ◽  
Author(s):  
Wolfgang Frenzel ◽  
Jürgen Schulz-Brüssel ◽  
Betty Zinvirt
Keyword(s):  
Optical Flow ◽  
Gas Diffusion ◽  
Flow Through ◽  
Diffusion Membrane
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