Advanced Deep Learning for Pose Estimation: From Hourglass Networks to BlazePose
In this video, I talk about state-of-the-art deep learning techniques for human pose estimation, exploring various models like the Stacked Hourglass Network and BlazePose. I discuss how these architectures are structured, from downsampling and upsampling stages to the multi-branch architecture in BlazePose. We also discuss the evolution of key frameworks, such as MobileNetV2's inverted residuals and efficient feature map handling.
00:00 DeepPose: Human Pose Estimation via Deep Neural Networks
4:11 Stacked Hourglass Networks for Human Pose Estimation
10:47 BlazePose: On-device Real-time Body Pose tracking introduction
11:00 MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
14:35 MobileNetV2: Inverted Residuals and Linear Bottlenecks
16:46 BlazePose: On-device Real-time Body Pose tracking