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Video Understanding

Voice Actor Erika Ishii on Video Game Roles and Motion Capture


This week, host Karen Han talks to voice actor and performer Erika Ishii, whose very long resume includes video games, animated series, and live action projects. In the interview, Erika explains their process of bringing video game characters to life–characters like Valkyrie in the game Apex Legends. Then Erika discusses diversity among both characters and performers in the video game industry and the ability to say no to projects that aren't the right fit. After the interview, Karen and co-host Isaac Butler talk about diversity in entertainment and the progress that has yet to be made. In the exclusive Slate Plus segment, Erika lists some of the voice actors and performances that have inspired them over the years.

A one-up on motion capture


From "Star Wars" to "Happy Feet," many beloved films contain scenes that were made possible by motion capture technology, which records movement of objects or people through video. Further, applications for this tracking, which involve complicated interactions between physics, geometry, and perception, extend beyond Hollywood to the military, sports training, medical fields, and computer vision and robotics, allowing engineers to understand and simulate action happening within real-world environments. As this can be a complex and costly process -- often requiring markers placed on objects or people and recording the action sequence -- researchers are working to shift the burden to neural networks, which could acquire this data from a simple video and reproduce it in a model. Work in physics simulations and rendering shows promise to make this more widely used, since it can characterize realistic, continuous, dynamic motion from images and transform back and forth between a 2D render and 3D scene in the world. However, to do so, current techniques require precise knowledge of the environmental conditions where the action is taking place, and the choice of renderer, both of which are often unavailable.

This Echo Dot and Ring Video Doorbell bundle is 55% off

Daily Mail - Science & tech

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Best video doorbell (2022)


The home security industry has been revolutionized with new technology, ranging from smart home sensors to smart locks to security video cameras. But one of the most subtle and effective security measures you can add to your home is a video camera doorbell. These doorbells serve two purposes: they record footage of people who approach your front door, and they alert you with a pleasant chime or jingle when they're pressed -- just like a regular doorbell -- and have a video recording of everything just in case. Although these devices can be very useful and increase your home's security, it can be tough to determine which video doorbell you should pick. Luckily for you, we've put together a selection of the top video doorbells in 2022.

Eufy's Video Doorbell Dual Keeps One Eye on Your Packages


A video doorbell can be a great help, and we've reviewed a bunch of them. They can let you know when a delivery has arrived, but they can't always ensure that your package will still be there when you get home. Eufy's dual video doorbell solves this problem by adding a second camera, so you can get a clear look at visitors and monitor anything sitting on the doorstep. The split-screen view ensures comprehensive porch coverage and will relieve anyone who has package anxiety. It's also refreshing to find a camera with on-device AI and local storage, so you never have to upload videos to the cloud.

The Best Video Doorbell Cameras


This doorbell has performed reliably over months of testing. It offers an expansive 180-degree square view of your front porch, swift alerts with clear notifications, and detailed video during the day and night. False positives are rare, and it never misses the action. The companion app is very straightforward and relatively quick to load a live view or recorded videos compared to other smart doorbell apps. I also appreciate that someone pressing the doorbell triggers a call on my phone.

Human pose estimation with 80% smaller model and 68% less CPU using STNet


Human pose estimation with %80 smaller model and 68% less CPU using STNet Towards Simple and Accurate Human Pose Estimation with Stair Network arXiv paper abstract arXiv PDF paper In ... keypoint coordinates regression task. ... existing approaches adopt complicated networks with a large number of parameters, leading to a heavy model with poor cost-effectiveness in practice. ... To overcome ... develop a small yet discrimi

Protect your home with this Eufy video doorbell for $153


Looking to protect your home? Amazon is selling the Eufy Security 2K Video Doorbell for $153. In our review of the Eufy 2k Video Doorbell, we gave it four out of five stars. "The Eufy Security Wireless Video Doorbell delivers a great set of features, and its accompanying Homebase storage unit will eliminate the need for a cloud-storage subscription–at least for most people," we said. Unlike other video doorbells, Eufy doesn't require cloud storage to store all your videos.

A survey of top-down approaches for human pose estimation Artificial Intelligence

Human pose estimation in two-dimensional images videos has been a hot topic in the computer vision problem recently due to its vast benefits and potential applications for improving human life, such as behaviors recognition, motion capture and augmented reality, training robots, and movement tracking. Many state-of-the-art methods implemented with Deep Learning have addressed several challenges and brought tremendous remarkable results in the field of human pose estimation. Approaches are classified into two kinds: the two-step framework (top-down approach) and the part-based framework (bottom-up approach). While the two-step framework first incorporates a person detector and then estimates the pose within each box independently, detecting all body parts in the image and associating parts belonging to distinct persons is conducted in the part-based framework. This paper aims to provide newcomers with an extensive review of deep learning methods-based 2D images for recognizing the pose of people, which only focuses on top-down approaches since 2016. The discussion through this paper presents significant detectors and estimators depending on mathematical background, the challenges and limitations, benchmark datasets, evaluation metrics, and comparison between methods.

Towards a more applicative Pose Estimation.


Almost every pose estimation algorithm suffers from the problem of jitter during inference. The high-frequency oscillations of keypoints around a point characterize a noisy signal is known as jitter. The jitter cause can be attributed to the fact that we perform these inferences at a frame level for the entire video input. And these consecutive frames have varying occlusion (and a range of complex poses). Another reason can be the inconsistency in the annotations in training data that results in uncertainty in pose estimation.