A Benchmark of Motion Detection Algorithms for Static Camera: Application on CDnet 2012 Dataset

2018 ◽  
pp. 235-245
Author(s):  
Kamal Sehairi ◽  
Chouireb Fatima ◽  
Jean Meunier
Keyword(s):  
Motion Detection ◽  
Detection Algorithms

Erratum to “a quantitative comparison of motion detection algorithms in fMRI”

2003 ◽  
Vol 21 (5) ◽  
pp. 573
Author(s):  
Babak A. Ardekani ◽  
Alvin H. Bachman ◽  
Joseph A. Helpern
Keyword(s):  
Motion Detection ◽  
Quantitative Comparison ◽  
Detection Algorithms

scholarly journals Motion detection: cells, circuits and algorithms

Neuroforum ◽  
2018 ◽  
Vol 24 (2) ◽  
pp. A61-A72 ◽  
Author(s):  
Giordano Ramos-Traslosheros ◽  
Miriam Henning ◽  
Marion Silies
Keyword(s):  
Motion Detection ◽  
Visual Motion ◽  
Motion Cues ◽  
Detection Algorithms ◽  
Preferred Direction ◽  
Direct Measurements ◽  
Classical Models ◽  
Light Signals ◽  
Circuit Components ◽  
Visual Motion Cues

Abstract Many animals use visual motion cues to inform different behaviors. The basis for motion detection is the comparison of light signals over space and time. How a nervous system performs such spatiotemporal correlations has long been considered a paradigmatic neural computation. Here, we will first describe classical models of motion detection and introduce core motion detecting circuits in Drosophila. Direct measurements of the response properties of the first direction-selective cells in the Drosophila visual system have revealed new insights about the implementation of motion detection algorithms. Recent data suggest a combination of two mechanisms, a nonlinear enhancement of signals moving into the preferred direction, as well as a suppression of signals moving into the opposite direction. These findings as well as a functional analysis of the circuit components have shown that the microcircuits that process elementary motion are more complex than anticipated. Building on this, we have the opportunity to understand detailed properties of elementary, yet intricate microcircuits.

Odor motion sensing enables complex plume navigation

2021 ◽  
Author(s):  
Nirag Kadakia ◽  
Mahmut Demir ◽  
Brenden T. Michaelis ◽  
Matthew A. Reidenbach ◽  
Damon A. Clark ◽  
...  
Keyword(s):  
Motion Detection ◽  
Critical Role ◽  
Natural Environments ◽  
Orthogonal Direction ◽  
Directional Information ◽  
Insect Navigation ◽  
Detection Algorithms ◽  
Bilateral Differences ◽  
Odor Signals

ABSTRACTInsects can detect bilateral differences in odor concentration between their two antennae, enabling them to sense odor gradients. While gradients aid navigation in simple odor environments like static ribbons, their role in navigating complex plumes remains unknown. Here, we use a virtual reality paradigm to show that Drosophila use bilateral sensing for a distinct computation: detecting the motion of odor signals. Such odor direction sensing is computationally equivalent to motion detection algorithms underlying motion detection in vision. Simulations of natural plumes reveal that odor motion contains valuable directional information absent from the airflow, which Drosophila indeed exploit when navigating natural plumes. Olfactory studies dating back a century have stressed the critical role of wind sensing for insect navigation (Flügge, 1934; Kennedy and Marsh, 1974); we reveal an entirely orthogonal direction cue used by flies in natural environments, and give theoretical arguments suggesting that this cue may be of broad use across the animal kingdom.

RICA-MD: A Refined ICA Algorithm for Motion Detection

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-17
Author(s):  
Chao Zhang ◽  
Xiaopei Wu ◽  
Jianchao Lu ◽  
Xi Zheng ◽  
Alireza Jolfaei ◽  
...  
Keyword(s):  
Motion Detection ◽  
Moving Objects ◽  
Rapid Development ◽  
False Alarms ◽  
Analysis Method ◽  
Detection Algorithms ◽  
Complex Scenes ◽  
Statistical Analysis Method ◽  
Conventional Methods ◽  
Ica Algorithm

With the rapid development of various computing technologies, the constraints of data processing capabilities gradually disappeared, and more data can be simultaneously processed to obtain better performance compared to conventional methods. As a standard statistical analysis method that has been widely used in many fields, Independent Component Analysis (ICA) provides a new way for motion detection by extracting the foreground without precisely modeling the background. However, most existing ICA-based motion detection algorithms use only two-channel data for source separation and simply generate the observation vectors by decomposing and reconstructing the images by row, hence they cannot obtain an integrated and accurate shape of the moving objects in complex scenes. In this article, we propose a refined ICA algorithm for motion detection (RICA-MD), which fuses a larger number of channels than conventional ICA-based motion detection algorithms to provide more effective information for foreground extraction. Meanwhile, we propose four novel methods for generating observation vectors to further cover the diverse motion styles of the moving objects. These improvements enable RICA-MD to effectively deal with slowly moving objects, which are difficult to detect using conventional methods. Our quantitative evaluation in multiple scenes shows that our proposed method is able to achieve a better performance at an acceptable cost of false alarms.

scholarly journals A quantitative comparison of motion detection algorithms in fMRI

2001 ◽  
Vol 19 (7) ◽  
pp. 959-963 ◽  
Author(s):  
Babak A. Ardekani ◽  
Alvin H. Bachman ◽  
Joseph A. Helpern
Keyword(s):  
Motion Detection ◽  
Quantitative Comparison ◽  
Detection Algorithms

scholarly journals Motion detection algorithms using template model

2001 ◽  
Author(s):  
Hung Nguyen ◽  
Sreeja Rajesh ◽  
Derek Abbott
Keyword(s):  
Motion Detection ◽  
Template Model ◽  
Detection Algorithms
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