Dynamics Enhanced Multi-camera Motion Segmentation from Unsynchronized Videos

2017 ◽  
Author(s):  
Xikang Zhang ◽  
Bengisu Ozbay ◽  
Mario Sznaier ◽  
Octavia Camps
Keyword(s):  
Motion Segmentation ◽  
Camera Motion

scholarly journals A General Inlier Estimation for Moving Camera Motion Segmentation

2015 ◽  
Vol 7 (0) ◽  
pp. 163-174 ◽  
Author(s):  
Xuefeng Liang ◽  
Cuicui Zhang ◽  
Takashi Matsuyama
Keyword(s):  
Motion Segmentation ◽  
Camera Motion ◽  
Moving Camera

scholarly journals Kinematic Analysis of Lower Limb Joint Asymmetry during Gait in People with Multiple Sclerosis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 598
Author(s):  
Massimiliano Pau ◽  
Bruno Leban ◽  
Michela Deidda ◽  
Federica Putzolu ◽  
Micaela Porta ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Lower Limb ◽  
Sagittal Plane ◽  
Cross Sectional Study ◽  
Camera Motion ◽  
Cross Sectional ◽  
Temporal Parameters ◽  
Wide Range ◽  
Edss Score ◽  
Spatio Temporal

The majority of people with Multiple Sclerosis (pwMS), report lower limb motor dysfunctions, which may relevantly affect postural control, gait and a wide range of activities of daily living. While it is quite common to observe a different impact of the disease on the two limbs (i.e., one of them is more affected), less clear are the effects of such asymmetry on gait performance. The present retrospective cross-sectional study aimed to characterize the magnitude of interlimb asymmetry in pwMS, particularly as regards the joint kinematics, using parameters derived from angle-angle diagrams. To this end, we analyzed gait patterns of 101 pwMS (55 women, 46 men, mean age 46.3, average Expanded Disability Status Scale (EDSS) score 3.5, range 1–6.5) and 81 unaffected individuals age- and sex-matched who underwent 3D computerized gait analysis carried out using an eight-camera motion capture system. Spatio-temporal parameters and kinematics in the sagittal plane at hip, knee and ankle joints were considered for the analysis. The angular trends of left and right sides were processed to build synchronized angle–angle diagrams (cyclograms) for each joint, and symmetry was assessed by computing several geometrical features such as area, orientation and Trend Symmetry. Based on cyclogram orientation and Trend Symmetry, the results show that pwMS exhibit significantly greater asymmetry in all three joints with respect to unaffected individuals. In particular, orientation values were as follows: 5.1 of pwMS vs. 1.6 of unaffected individuals at hip joint, 7.0 vs. 1.5 at knee and 6.4 vs. 3.0 at ankle (p < 0.001 in all cases), while for Trend Symmetry we obtained at hip 1.7 of pwMS vs. 0.3 of unaffected individuals, 4.2 vs. 0.5 at knee and 8.5 vs. 1.5 at ankle (p < 0.001 in all cases). Moreover, the same parameters were sensitive enough to discriminate individuals of different disability levels. With few exceptions, all the calculated symmetry parameters were found significantly correlated with the main spatio-temporal parameters of gait and the EDSS score. In particular, large correlations were detected between Trend Symmetry and gait speed (with rho values in the range of –0.58 to –0.63 depending on the considered joint, p < 0.001) and between Trend Symmetry and EDSS score (rho = 0.62 to 0.69, p < 0.001). Such results suggest not only that MS is associated with significantly marked interlimb asymmetry during gait but also that such asymmetry worsens as the disease progresses and that it has a relevant impact on gait performances.

scholarly journals Imperceptible manipulation of lateral camera motion for improved virtual reality applications

2020 ◽  
Vol 39 (6) ◽  
pp. 1-14
Author(s):  
Ana Serrano ◽  
Daniel Martin ◽  
Diego Gutierrez ◽  
Karol Myszkowski ◽  
Belen Masia
Keyword(s):  
Virtual Reality ◽  
Camera Motion

Human motion segmentation based on structure constraint matrix factorization

2021 ◽  
Vol 65 (1) ◽  
Author(s):  
Hongbo Gao ◽  
Fang Guo ◽  
Juping Zhu ◽  
Zhen Kan ◽  
Xinyu Zhang
Keyword(s):  
Matrix Factorization ◽  
Motion Segmentation ◽  
Human Motion ◽  
Constraint Matrix ◽  
Structure Constraint

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

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 507
Author(s):  
Le Wang ◽  
Lirong Xiang ◽  
Lie Tang ◽  
Huanyu Jiang
Keyword(s):  
Frame Rate ◽  
Flying Height ◽  
Battery Life ◽  
Growth Stages ◽  
Camera Motion ◽  
Long Duration ◽  
Manual Counting ◽  
Detection And Counting ◽  
Plant Image ◽  
Early Seedling

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. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting.

Intelligent English classroom video clarity improvement based on motion compensation and grid flow

2020 ◽  
pp. 1-11
Author(s):  
Shufang Li ◽  
Wang Juan
Keyword(s):  
Motion Compensation ◽  
Noise Removal ◽  
Operating Efficiency ◽  
Camera Motion ◽  
Video Denoising ◽  
Actual Operating ◽  
Flow Motion ◽  
Pros And Cons ◽  
English Classroom ◽  
Teaching Video

For the English classroom teaching video denoising algorithm, it is not only necessary to consider whether the noise removal of the output video is thorough, but also to consider the actual operating efficiency and robustness of the algorithm. In the process of the thesis research, after reading a large number of internal and external documents on video denoising algorithms and analyzing the pros and cons of various denoising algorithms, this paper proposes a new video denoising algorithm, which uses the recently proposed grid flow motion model based on camera motion compensation to generate denoised video. Compared with the current advanced video denoising schemes, our method processes noisy frames faster and has good robustness. In addition, this article improves the algorithm framework so that the algorithm can not only deal with offline video denoising, but also deal with online video denoising.

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