Investigation of Different Skeleton Features for CNN-based 3D Action Recognition

Deep learning techniques are being used in skeleton based action recognition tasks and outstanding performance has been reported. Compared with RNN based methods which tend to overemphasize temporal information, CNN-based approaches can jointly capture spatio-temporal information from texture color images encoded from skeleton sequences. There are several skeleton-based features that have proven effective in RNN-based and handcrafted-feature-based methods. However, it remains unknown whether they are suitable for CNN-based approaches. This paper proposes to encode five spatial skeleton features into images with different encoding methods. In addition, the performance implication of different joints used for feature extraction is studied. The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis. An accuracy of 75.32\% was achieved in Large Scale 3D Human Activity Analysis Challenge in Depth Videos

Joint 3D Proposal Generation and Object Detection from View Aggregation

We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. Our proposed architecture is shown to produce state of the art results on the KITTI 3D object detection benchmark while running in real time with a low memory footprint, making it a suitable candidate for deployment on autonomous vehicles. Code is at: https://github.com/kujason/avodComment: For any inquiries contact aharakeh(at)uwaterloo(dot)c