Results 11 to 20 of about 125,764 (312)

Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash

Sensors, 2021
In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury.
Christopher Brumann, Markus Kukuk, Claus Reinsberger   +2 more
doaj   +1 more source

Behavioral Pharmacology as the Main Approach to Study the Efficiency of Potential Psychotropic Drugs: Analysis of Modern Methods (Review)

Разработка и регистрация лекарственных средств, 2023
Introduction. Behavioral methods on laboratory animals are recognized as the main approach in studying the activity of potential psychotropic drugs and allow us to evaluate the main effects of new compounds, increase the possibility of predicting a ...
I. I. Semina, A. Z. Baichurina, D. O. Nikitin, A. V. Nikitina, R. I. Moustafine, V. V. Khutoryanskiy   +5 more
doaj   +1 more source

Seedling maize counting method in complex backgrounds based on YOLOV5 and Kalman filter tracking algorithm

Frontiers in Plant Science, 2022
Maize population density is one of the most essential factors in agricultural production systems and has a significant impact on maize yield and quality. Therefore, it is essential to estimate maize population density timely and accurately.
Yang Li, Yang Li, Yang Li, Zhiyuan Bao, Zhiyuan Bao, Jiangtao Qi, Jiangtao Qi   +6 more
doaj   +1 more source

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Sensors, 2021
Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error.
Le Wang, Lirong Xiang, Lie Tang, Huanyu Jiang   +3 more
doaj   +1 more source

Automated tracking of aquatic crustaceans with potential application on the quantification of animals movement

Ecología Austral, 2022
Here, we present a set of algorithms using the Python programming language, that will allow using a routine for object detection and tracking in experimental videos.
Jesús D. Nuñez, Octavio Massone, José A. García   +2 more
doaj   +1 more source

Measures of Effective Video Tracking [PDF]

IEEE Transactions on Image Processing, 2014
To evaluate multitarget video tracking results, one needs to quantify the accuracy of the estimated target-size and the cardinality error as well as measure the frequency of occurrence of ID changes. In this paper, we survey existing multitarget tracking performance scores and, after discussing their limitations, we propose three parameter-independent ...
Nawaz, Tahir, Cavallaro, Andrea, Poiesi, Fabio   +2 more
openaire   +3 more sources

Progressive tracking: a novel procedure to facilitate manual digitization of videos

Biology Open, 2020
Digitization of video recordings often requires the laborious procedure of manually clicking points of interest on individual video frames. Here, we present progressive tracking, a procedure that facilitates manual digitization of markerless videos.
Maja Mielke, Peter Aerts, Chris Van Ginneken, Sam Van Wassenbergh, Falk Mielke   +4 more
doaj   +1 more source

Object Tracking Based on Satellite Videos: A Literature Review

Remote Sensing, 2022
Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events.
Zhaoxiang Zhang, Chenghang Wang, Jianing Song, Yuelei Xu   +3 more
doaj   +1 more source

Multi-detector Fusion-based Depth Correlation Filtering Video Multi-target Tracking Algorithm [PDF]

Jisuanji kexue, 2022
In the detection and tracking task,the detector has mis-detected and missed targets.For video multi-target tracking algorithms that rely on detection information,there will be a large number of false tracking targets and missed targets.Such missed and ...
SHEN Xiang-pei, DING Yan-rui
doaj   +1 more source

Video Tracking Using Learned Hierarchical Features [PDF]

IEEE Transactions on Image Processing, 2015
In this paper, we propose an approach to learn hierarchical features for visual object tracking. First, we offline learn features robust to diverse motion patterns from auxiliary video sequences. The hierarchical features are learned via a two-layer convolutional neural network.
Wang, Gang, Chan, Kap Luk, Liu, Ting, Yang, Qingxiong, Wang, Li   +4 more
openaire   +3 more sources