The Elderly Fall Detection Algorithm Based on Human Joint Extraction and Object Detection

Nowadays, the care of the elderly has become a social concern. The fall of the elderly has become one of the main factors threatening the health of the elderly. In this paper, we designed a fall detection algorithm based on human joint extraction and object detection.First,yolov4 was used to identify and detect the elderly. Then openpose was used to detect the human joint. Based on the human joint, this paper using Random Forest to classify the status of the elderly, there are three states of the elderly: falling down, lying down and other states. In the detection of a single old man, the accuracy of the model reached 99.3%, the sensitivity and specificity of the model reached 79.3% and 72.1%

SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos

Camera-based 3D object detection in BEV (Bird's Eye View) space has drawn great attention over the past few years. Dense detectors typically follow a two-stage pipeline by first constructing a dense BEV feature and then performing object detection in BEV space, which suffers from complex view transformations and high computation cost. On the other side, sparse detectors follow a query-based paradigm without explicit dense BEV feature construction, but achieve worse performance than the dense counterparts. In this paper, we find that the key to mitigate this performance gap is the adaptability of the detector in both BEV and image space. To achieve this goal, we propose SparseBEV, a fully sparse 3D object detector that outperforms the dense counterparts. SparseBEV contains three key designs, which are (1) scale-adaptive self attention to aggregate features with adaptive receptive field in BEV space, (2) adaptive spatio-temporal sampling to generate sampling locations under the guidance of queries, and (3) adaptive mixing to decode the sampled features with dynamic weights from the queries. On the test split of nuScenes, SparseBEV achieves the state-of-the-art performance of 67.5 NDS. On the val split, SparseBEV achieves 55.8 NDS while maintaining a real-time inference speed of 23.5 FPS. Code is available at https://github.com/MCG-NJU/SparseBEV.Comment: Accepted to ICCV 202

Constraints on the rotating self-dual black hole with quasi-periodic oscillations

An impressive feature of loop quantum gravity (LQG) is that it can elegantly resolve both the big bang and black hole singularities. By using the Newman-Janis algorithm, a regular and effective rotating self-dual black hole(SDBH) metric could be constructed, which alters the Kerr geometry with a polymeric function $P$ from the quantum effects of LQG geometry. In this paper, we investigate its impact on the frequency characteristics of the X-ray quasi-periodic oscillations(QPOs) from 5 X-ray binaries and contrast it with the existing results of the orbital, periastron precession and nodal precession frequencies within the relativistic precession model. We apply a Monte Carlo Markov Chain (MCMC) simulation to examine the possible LQG effects on the X-ray QPOs. We found that the best constraint result for the rotating self-dual geometry from LQG came from the QPOs of X-ray binary GRO J1655-40, which establish an upper bound on the polymeric function $P$ less than $8.6\times 10^{-4}$ at 95\% confidence level. This bound leads to a restriction on the polymeric parameter $\delta$ of LQG to be 0.24

Exploring the Cosmic Reionization Epoch in Frequency Space: An Improved Approach to Remove the Foreground in 21 cm Tomography

Aiming to correctly restore the redshifted 21 cm signals emitted by the neutral hydrogen during the cosmic reionization processes, we re-examine the separation approaches based on the quadratic polynomial fitting technique in frequency space to investigate whether they works satisfactorily with complex foreground, by quantitatively evaluate the quality of restored 21 cm signals in terms of sample statistics. We construct the foreground model to characterize both spatial and spectral substructures of the real sky, and use it to simulate the observed radio spectra. By comparing between different separation approaches through statistical analysis of restored 21 cm spectra and corresponding power spectra, as well as their constraints on the mean halo bias $b$ and average ionization fraction $x_e$ of the reionization processes, at $z=8$ and the noise level of 60 mK we find that, although the complex foreground can be well approximated with quadratic polynomial expansion, a significant part of Mpc-scale components of the 21 cm signals (75% for $\gtrsim 6h^{-1}$ Mpc scales and 34% for $\gtrsim 1h^{-1}$ Mpc scales) is lost because it tends to be mis-identified as part of the foreground when single-narrow-segment separation approach is applied. The best restoration of the 21 cm signals and the tightest determination of $b$ and $x_e$ can be obtained with the three-narrow-segment fitting technique as proposed in this paper. Similar results can be obtained at other redshifts.Comment: 33 pages, 14 figures. Accepted for publication in Ap