Your front door is your home's first line of defense. Having a video doorbell mounted next to that door is almost as important as having a deadbolt, because it will not only give your visitors an easy way to let you know they're there, but it will also know when anyone approaches your home–whether or not you're home at the time. In fact, these cameras are so useful you might want to mount one next to every entry point into your home: side entrances, at your garage door, and the door to your backyard, for example. Whether you're waiting for friends to visit, watching for trouble-makers, tracking parcel deliveries, or hiding from that weird neighbor who keeps asking to borrow your lawn mower, the video doorbell is an essential security tool. TechHive's editors and contributors have been testing video doorbells since 2014, and we continuously evaluate the latest devices along with their accompanying apps.

Amazon is running a sale on its Blink home security devices. Among the items that have seen a price drop is the Blink Video Doorbell, which is available for a record low of 30. That's half what you might otherwise pay for it. The doorbell allows you to answer your door using your phone. You can see who rang your doorbell via a 1080p video stream (there's an infrared night vision mode) and chat to them using the two-way audio feature.

SAVE 70: The Arlo Video Doorbell is on sale at Amazon for just 59.99, down from the list price of 129.99. That's a 54% discount that matches the lowest price we've ever seen at Amazon. The Amazon Big Spring Sale is officially over, but Amazon has an olive branch for those of us wo didn't get around to shopping the sale. If you have plans to ramp up home security, check out this still-live Amazon Spring Sale deal. As of April 1, the Arlo Video Doorbell is still just 59.99, marked down from the normal price of 129.99.

We have included in the supplementary material the parts that we could not mention in the main paper. Section A covers the implementation details, Section B presents additional experiments, and Section C describes the detailed annotation process. Lastly, we have included a description of the license and ethical considerations in the Section D.

Multi-person pose estimation and tracking have been actively researched by the computer vision community due to their practical applicability. However, existing human pose estimation and tracking datasets have only been successful in typical scenarios, such as those without motion blur or with well-lit conditions. These RGB-based datasets are limited to learning under extreme motion blur situations or poor lighting conditions, making them inherently vulnerable to such scenarios.

For tackling the task of 2D human pose estimation, the great majority of the recent methods regard this task as a heatmap estimation problem, and optimize the heatmap prediction using the Gaussian-smoothed heatmap as the optimization objective and using the pixel-wise loss (e.g.

Inferring the 3D structure underlying a set of multi-view images typically requires solving two co-dependent tasks - accurate 3D reconstruction requires precise camera poses, and predicting camera poses relies on (implicitly or explicitly) modeling the underlying 3D. The classical framework of analysis by synthesis casts this inference as a joint optimization seeking to explain the observed pixels, and recent instantiations learn expressive 3D representations (e.g., Neural Fields) with gradient-descent-based pose refinement of initial pose estimates. However, given a sparse set of observed views, the observations may not provide sufficient direct evidence to obtain complete and accurate 3D. Moreover, large errors in pose estimation may not be easily corrected and can further degrade the inferred 3D.

Research on egocentric tasks in computer vision has mostly focused on headmounted cameras, such as fisheye cameras or embedded cameras inside immersive headsets. We argue that the increasing miniaturization of optical sensors will lead to the prolific integration of cameras into many more body-worn devices at various locations. This will bring fresh perspectives to established tasks in computer vision and benefit key areas such as human motion tracking, body pose estimation, or action recognition--particularly for the lower body, which is typically occluded. In this paper, we introduce EgoSim, a novel simulator of body-worn cameras that generates realistic egocentric renderings from multiple perspectives across a wearer's body. A key feature of EgoSim is its use of real motion capture data to render motion artifacts, which are especially noticeable with armor leg-worn cameras. In addition, we introduce MultiEgoView, a dataset of egocentric footage from six body-worn cameras and ground-truth full-body 3D poses during several activities: 119 hours of data are derived from AMASS motion sequences in four high-fidelity virtual environments, which we augment with 5 hours of real-world motion data from 13 participants using six GoPro cameras and 3D body pose references from an Xsens motion capture suit. We demonstrate EgoSim's effectiveness by training an end-to-end video-only 3D pose estimation network. Analyzing its domain gap, we show that our dataset and simulator substantially aid training for inference on real-world data.

In the field of 3D Human Pose Estimation (HPE), scalability and generalization across diverse real-world scenarios remain significant challenges. This paper addresses two key bottlenecks to scalability: limited data diversity caused by'popularity bias' and increased'one-to-many' depth ambiguity arising from greater pose diversity. We introduce the Biomechanical Pose Generator (BPG), which leverages biomechanical principles, specifically the normal range of motion, to autonomously generate a wide array of plausible 3D poses without relying on a source dataset, thus overcoming the restrictions of popularity bias. To address depth ambiguity, we propose the Binary Depth Coordinates (BDC), which simplifies depth estimation into a binary classification of joint positions (front or back). This method decomposes a 3D pose into three core elements--2D pose, bone length, and binary depth decision--substantially reducing depth ambiguity and enhancing model robustness and accuracy, particularly in complex poses. Our results demonstrate that these approaches increase the diversity and volume of pose data while consistently achieving performance gains, even amid the complexities introduced by increased pose diversity.

Heatmap regression has dominated human pose estimation due to its superior performance and strong generalization. To meet the requirements of traditional explicit neural networks for output form, existing heatmap-based methods discretize the originally continuous heatmap representation into 2D pixel arrays, which leads to performance degradation due to the introduction of quantization errors. This problem is significantly exacerbated as the size of the input image decreases, which makes heatmap-based methods not much better than coordinate regression on low-resolution images. In this paper, we propose a novel neural representation for human pose estimation called NerPE to achieve continuous heatmap regression. Given any position within the image range, NerPE regresses the corresponding confidence scores for body joints according to the surrounding image features, which guarantees continuity in space and confidence during training. Thanks to the decoupling from spatial resolution, NerPE can output the predicted heatmaps at arbitrary resolution during inference without retraining, which easily achieves sub-pixel localization precision. To reduce the computational cost, we design progressive coordinate decoding to cooperate with continuous heatmap regression, in which localization no longer requires the complete generation of high-resolution heatmaps.