7 research outputs found
Real-World Repetition Estimation by Div, Grad and Curl
We consider the problem of estimating repetition in video, such as performing
push-ups, cutting a melon or playing violin. Existing work shows good results
under the assumption of static and stationary periodicity. As realistic video
is rarely perfectly static and stationary, the often preferred Fourier-based
measurements is inapt. Instead, we adopt the wavelet transform to better handle
non-static and non-stationary video dynamics. From the flow field and its
differentials, we derive three fundamental motion types and three motion
continuities of intrinsic periodicity in 3D. On top of this, the 2D perception
of 3D periodicity considers two extreme viewpoints. What follows are 18
fundamental cases of recurrent perception in 2D. In practice, to deal with the
variety of repetitive appearance, our theory implies measuring time-varying
flow and its differentials (gradient, divergence and curl) over segmented
foreground motion. For experiments, we introduce the new QUVA Repetition
dataset, reflecting reality by including non-static and non-stationary videos.
On the task of counting repetitions in video, we obtain favorable results
compared to a deep learning alternative
Context-aware and Scale-insensitive Temporal Repetition Counting
Temporal repetition counting aims to estimate the number of cycles of a given
repetitive action. Existing deep learning methods assume repetitive actions are
performed in a fixed time-scale, which is invalid for the complex repetitive
actions in real life. In this paper, we tailor a context-aware and
scale-insensitive framework, to tackle the challenges in repetition counting
caused by the unknown and diverse cycle-lengths. Our approach combines two key
insights: (1) Cycle lengths from different actions are unpredictable that
require large-scale searching, but, once a coarse cycle length is determined,
the variety between repetitions can be overcome by regression. (2) Determining
the cycle length cannot only rely on a short fragment of video but a contextual
understanding. The first point is implemented by a coarse-to-fine cycle
refinement method. It avoids the heavy computation of exhaustively searching
all the cycle lengths in the video, and, instead, it propagates the coarse
prediction for further refinement in a hierarchical manner. We secondly propose
a bidirectional cycle length estimation method for a context-aware prediction.
It is a regression network that takes two consecutive coarse cycles as input,
and predicts the locations of the previous and next repetitive cycles. To
benefit the training and evaluation of temporal repetition counting area, we
construct a new and largest benchmark, which contains 526 videos with diverse
repetitive actions. Extensive experiments show that the proposed network
trained on a single dataset outperforms state-of-the-art methods on several
benchmarks, indicating that the proposed framework is general enough to capture
repetition patterns across domains.Comment: Accepted by CVPR202
Human Gait Modeling, Prediction and Classification for Level Walking Using Harmonic Models Derived from a Single Thigh-Mounted IMU
The majority of human gait modeling is based on hip, foot or thigh acceleration. The regeneration accuracy of these modeling approaches is not very high. This paper presents a harmonic approach to modeling human gait during level walking based on gyroscopic signals for a single thigh-mounted Inertial Measurement Unit (IMU) and the flexion–extension derived from a single thigh-mounted IMU. The thigh angle can be modeled with five significant harmonics, with a regeneration accuracy of over 0.999 correlation and less than 0.5◦ RMSE per stride cycle. Comparable regeneration accuracies can be achieved with nine significant harmonics for the gyro signal. The fundamental frequency of the harmonic model can be estimated using the stride time, with an error level of 0.0479% (±0.0029%). Six commonly observed stride patterns, and harmonic models of thigh angle and gyro signal for those stride patterns, are presented in this paper. These harmonic models can be used to predict or classify the strides of walking trials, and the results are presented herein. Harmonic models may also be used for activity recognition. It has shown that human gait in level walking can be modeled with a harmonic model of thigh angle or gyro signal, using a single thigh-mounted IMU, to higher accuracies than existing techniques
Human gait modelling with step estimation and phase classification utilising a single thigh mounted IMU for vision impaired indoor navigation
This research is focused on human gait modelling for infrastructure free inertial navigation for vision impaired. A pedometer based on a single thigh mounted gyroscope, an efficient algorithm to estimate thigh flexion and extension, gait models for level walking, a model to estimate step length and a technique to detect gait phases based on a single thigh mounted Inertial Measurement Unit (IMU) were developed and confirmed higher accuracies