Deep spectral feature pyramid in the frequency domain for long-term action recognition

2019 ◽  
Vol 64 ◽  
pp. 102650
Author(s):  
Gaoyun An ◽  
Zhenxing Zheng ◽  
Dapeng Wu ◽  
Wen Zhou
Keyword(s):  
Frequency Domain ◽  
Action Recognition ◽  
Spectral Feature ◽  
Feature Pyramid

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

2021 ◽  
Vol 63 (8) ◽  
pp. 465-471
Author(s):  
Shang Zhiwu ◽  
Yu Yan ◽  
Geng Rui ◽  
Gao Maosheng ◽  
Li Wanxiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Extraction Method ◽  
Dynamic Time Warping ◽  
Spectral Feature ◽  
Time Warping ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

scholarly journals Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100

2021 ◽  
Author(s):  
Dima Damen ◽  
Hazel Doughty ◽  
Giovanni Maria Farinella ◽  
Antonino Furnari ◽  
Evangelos Kazakos ◽  
...  
Keyword(s):  
Action Recognition ◽  
Domain Adaptation ◽  
Weak Supervision ◽  
Action Detection ◽  
Previous Version ◽  
Fine Grained ◽  
Unsupervised Domain Adaptation ◽  
Egocentric Vision ◽  
New Challenges

AbstractThis paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (Damen in Scaling egocentric vision: ECCV, 2018), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection enables new challenges such as action detection and evaluating the “test of time”—i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later. The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics.

scholarly journals Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition

2020 ◽  
Author(s):  
Zachary V Johnson ◽  
Lijiang Long ◽  
Junyu Li ◽  
Manu Tej Sharma Arrojwala ◽  
Vineeth Aljapur ◽  
...  
Keyword(s):  
Decision Making ◽  
Action Recognition ◽  
Lake Malawi ◽  
Simultaneous Recording ◽  
Video Data ◽  
Automated System ◽  
Depth Sensing ◽  
Sand Surface ◽  
In The Wild

ABSTRACTMeasuring naturalistic behaviors in laboratory settings is difficult, and this hinders progress in understanding decision-making in response to ecologically-relevant stimuli. In the wild, many animals manipulate their environment to create architectural constructions, which represent a type of extended phenotype affecting survival and/or reproduction, and these behaviors are excellent models of goal-directed decision-making. Here, we describe an automated system for measuring bower construction in Lake Malawi cichlid fishes, whereby males construct sand structures to attract mates through the accumulated actions of thousands of individual sand manipulation decisions over the course of many days. The system integrates two orthogonal methods, depth sensing and action recognition, to simultaneously measure the developing bower structure and classify the sand manipulation decisions through which it is constructed. We show that action recognition accurately (>85%) classifies ten sand manipulation behaviors across three different species and distinguishes between scooping and spitting events that occur during bower construction versus feeding. Registration of depth and video data streams enables topographical mapping of these behaviors onto a dynamic 3D sand surface. The hardware required for this setup is inexpensive (<$250 per setup), allowing for the simultaneous recording from many independent aquariums. We further show that bower construction behaviors are non-uniform in time, non-uniform in space, and spatially repeatable across trials. We also quantify a unique behavioral phenotype in interspecies hybrids, wherein males sequentially express both phenotypes of behaviorally-divergent parental species. Our work demonstrates that simultaneously tracking both structure and behavior provides an integrated picture of long-term goal-directed decision-making in a naturalistic, dynamic, and social environment.

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