‘PhysTrack’: a Matlab based environment for video tracking of kinematics in the physics laboratory

2017 ◽  
Vol 38 (4) ◽  
pp. 045007 ◽  
Author(s):  
Muhammad Umar Hassan ◽  
Muhammad Sabieh Anwar
Keyword(s):  
Video Tracking ◽  
Physics Laboratory

Improving Experimental the General Physics Laboratory by Using a Consumer Grade Camera and Video Tracking Analysis Open Program

2017 ◽  
Vol 67 (10) ◽  
pp. 1210-1219
Author(s):  
Juyeong KIM ◽  
Janghee PARK ◽  
Hyewon LEE ◽  
Chung-sik KIM ◽  
Bog G. KIM*
Keyword(s):  
Video Tracking ◽  
General Physics ◽  
Physics Laboratory ◽  
Open Program

scholarly journals Quantification of early learning and movement sub-structure predictive of motor performance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vikram Jakkamsetti ◽  
William Scudder ◽  
Gauri Kathote ◽  
Qian Ma ◽  
Gustavo Angulo ◽  
...  
Keyword(s):  
Motor Performance ◽  
Support Vector Machine Classifier ◽  
Motor Impairment ◽  
Approximate Entropy ◽  
Video Tracking ◽  
Support Vector ◽  
Learning Support ◽  
Arc Length ◽  
Vertical Movements ◽  
Movement Smoothness

AbstractTime-to-fall off an accelerating rotating rod (rotarod) is widely utilized to evaluate rodent motor performance. We reasoned that this simple outcome could be refined with additional measures explicit in the task (however inconspicuously) to examine what we call movement sub-structure. Our goal was to characterize normal variation or motor impairment more robustly than by using time-to-fall. We also hypothesized that measures (or features) early in the sub-structure could anticipate the learning expected of a mouse undergoing serial trials. Using normal untreated and baclofen-treated movement-impaired mice, we defined these features and automated their analysis using paw video-tracking in three consecutive trials, including paw location, speed, acceleration, variance and approximate entropy. Spectral arc length yielded speed and acceleration uniformity. We found that, in normal mice, paw movement smoothness inversely correlated with rotarod time-to-fall for the three trials. Greater approximate entropy in vertical movements, and opposite changes in horizontal movements, correlated with greater first-trial time-to-fall. First-trial horizontal approximate entropy in the first few seconds predicted subsequent time-to-fall. This allowed for the separation, after only one rotarod trial, of different-weight, untreated mouse groups, and for the detection of mice otherwise unimpaired after baclofen, which displayed a time-to-fall similar to control. A machine-learning support vector machine classifier corroborated these findings. In conclusion, time-to-fall off a rotarod correlated well with several measures, including some obtained during the first few seconds of a trial, and some responsive to learning over the first two trials, allowing for predictions or preemptive experimental manipulations before learning completion.

HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking

2021 ◽  
Vol 30 ◽  
pp. 3056-3068
Author(s):  
Jia Shao ◽  
Bo Du ◽  
Chen Wu ◽  
Mingming Gong ◽  
Tongliang Liu
Keyword(s):  
High Resolution ◽  
Video Tracking ◽  
Siamese Network

scholarly journals Standardized Method for the Assessment of Behavioral Responses of Zebrafish Larvae

Biomedicines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 884
Author(s):  
Hideyuki Maeda ◽  
Noritoshi Fukushima ◽  
Akihiro Hasumi
Keyword(s):  
Early Stage ◽  
Scientific Evidence ◽  
Video Tracking ◽  
Abnormal Behavior ◽  
Laboratory Setting ◽  
Zebrafish Larvae ◽  
Experimental Systems ◽  
Standardized Method ◽  
Life Phenomena ◽  
And Behavior

Zebrafish are easy to breed in a laboratory setting as they are extremely fertile and produce dozens of eggs per set. Because zebrafish eggs and the skin of the early-stage larvae are transparent, their embryos and the hearts and muscles of their larvae can be easily observed. Multiple rapid analyses of heart rate and behavior can be performed on these larvae simultaneously, enabling investigation of the influence of neuroactive substances on abnormal behavior, death, and associated pathogenetic mechanisms. Zebrafish larvae are becoming increasingly popular among researchers and are used in laboratories worldwide to study various vertebrate life phenomena; more experimental systems using zebrafish will undoubtedly be developed in the future. However, based on the available literature, we believe that the conceptualization of a protocol based on scientific evidence is necessary to achieve standardization. We exposed zebrafish larvae at 6–7 days post-fertilization to 50 repeated light–dark stimuli at either 15-min or 5-min intervals. We measured the traveled distance and habituation time through a video tracking apparatus. The traveled distance stabilized after the 16th repetition when the zebrafish were exposed to light–dark stimuli at 15-min intervals and after the 5th repetition when exposed at 5-min intervals. Additionally, at 15-min intervals, the peak of the traveled distance was reached within the first minute in a dark environment, whereas at 5-min intervals, it did not reach the peak even after 5 min. The traveled distance was more stable at 5-min intervals of light/dark stimuli than at 15-min intervals. Therefore, if one acclimatizes zebrafish larvae for 1 h and collects data from the 5th repetition of light/dark stimuli at intervals of 5 min in the light/dark test, a stable traveled distance result can be obtained. The establishment of this standardized method would be beneficial for investigating substances of unknown lethal concentration.

scholarly journals A minimal 3D model of mosquito flight behaviour around the human baited bed net

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jeff Jones ◽  
Gregory P D Murray ◽  
Philip J McCall
Keyword(s):  
Spatial Scales ◽  
Video Tracking ◽  
Sub Saharan Africa ◽  
Bed Nets ◽  
State Transitions ◽  
Bed Net ◽  
Flight Behaviour ◽  
Individual Agent ◽  
Host Seeking ◽  
Mosquito Behaviour

Abstract Background Advances in digitized video-tracking and behavioural analysis have enabled accurate recording and quantification of mosquito flight and host-seeking behaviours, facilitating development of individual (agent) based models at much finer spatial scales than previously possible. Methods Quantified behavioural parameters were used to create a novel virtual testing model, capable of accurately simulating indoor flight behaviour by a virtual population of host-seeking mosquitoes as they interact with and respond to simulated stimuli from a human-occupied bed net. The model is described, including base mosquito behaviour, state transitions, environmental representation and host stimulus representation. Results In the absence of a bed net and human host bait, flight distribution of the model population was relatively uniform throughout the arena. Introducing an unbaited untreated bed net induced a change in distribution with an increase in landing events on the net surface, predominantly on the sides of the net. Adding the presence of a simulated human bait dramatically impacted flight distribution patterns, exploratory foraging and, the number and distribution of landing positions on the net, which were determined largely by the orientation of the human within. The model replicates experimental results with free-flying living mosquitoes at human-occupied bed nets, where contact occurs predominantly on the top surface of the net. This accuracy is important as it quantifies exposure to the lethal insecticide residues that may be unique to the net roof (or theoretically any other surface). Number of net contacts and height of contacts decreased with increasing attractant dispersal noise. Conclusions Results generated by the model are an accurate representation of actual mosquito behaviour recorded at and around a human-occupied bed net in untreated and insecticide-treated nets. This fine-grained model is highly flexible and has significant potential for in silico screening of novel bed net designs, potentially reducing time and cost and accelerating the deployment of new and more effective tools for protecting against malaria in sub-Saharan Africa.

scholarly journals Possibility of Autonomous Estimation of Shiba Goat’s Estrus and Non-Estrus Behavior by Machine Learning Methods

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 771
Author(s):  
Toshiya Arakawa
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Markov Models ◽  
Tracking System ◽  
Video Tracking ◽  
Training Data ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

Mammalian behavior is typically monitored by observation. However, direct observation requires a substantial amount of effort and time, if the number of mammals to be observed is sufficiently large or if the observation is conducted for a prolonged period. In this study, machine learning methods as hidden Markov models (HMMs), random forests, support vector machines (SVMs), and neural networks, were applied to detect and estimate whether a goat is in estrus based on the goat’s behavior; thus, the adequacy of the method was verified. Goat’s tracking data was obtained using a video tracking system and used to estimate whether they, which are in “estrus” or “non-estrus”, were in either states: “approaching the male”, or “standing near the male”. Totally, the PC of random forest seems to be the highest. However, The percentage concordance (PC) value besides the goats whose data were used for training data sets is relatively low. It is suggested that random forest tend to over-fit to training data. Besides random forest, the PC of HMMs and SVMs is high. However, considering the calculation time and HMM’s advantage in that it is a time series model, HMM is better method. The PC of neural network is totally low, however, if the more goat’s data were acquired, neural network would be an adequate method for estimation.

Video tracking using morphological pyramids

1999 ◽  
Vol 8 (2) ◽  
pp. 176 ◽  
Author(s):  
C. Andrew Segall
Keyword(s):  
Video Tracking

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

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