Fig. 1. Snapshots of our online marker-based hand tracking system on sequences with two-handed and hand-object interactions. We demonstrate a novel marker-labeling and tracking system that enables fully-automatic, real-time estimation of hand poses in challenging interaction scenarios with frequent occlusions. Markers labeled as left hand and right hand are rendered as orange and blue spheres respectively, while markers associated with predefined rigid bodies are rendered as green spheres.Optical marker-based motion capture is the dominant way for obtaining high-fidelity human body animation for special effects, movies, and video games. However, motion capture has seen limited application to the human hand due to the difficulty of automatically identifying (or labeling) identical markers on self-similar fingers. We propose a technique that frames the labeling problem as a keypoint regression problem conducive to a solution using convolutional neural networks. We demonstrate robustness of our labeling solution to occlusion, ghost markers, hand shape, and even motions involving two hands or handheld objects. Our technique is equally applicable to sparse or dense marker sets and can run in real-time to support interaction prototyping with high-fidelity hand tracking and hand presence in virtual reality.

We present a system for real-time hand-tracking to drive virtual and augmented reality (VR/AR) experiences. Using four fisheye monochrome cameras, our system generates accurate and low-jitter 3D hand motion across a large working volume for a diverse set of users. We achieve this by proposing neural network architectures for detecting hands and estimating hand keypoint locations. Our hand detection network robustly handles a variety of real world environments. The keypoint estimation network leverages tracking history to produce spatially and temporally consistent poses. We design scalable, semi-automated mechanisms to collect a large and diverse set of ground truth data using a combination of manual annotation and automated tracking. Additionally, we introduce a detection-by-tracking method that increases smoothness while reducing the computational cost; the optimized system runs at 60Hz on PC and 30Hz on a mobile processor. Together, these contributions yield a practical system for capturing a user's hands and is the default feature on the Oculus Quest VR headset powering input and social presence.

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A novel two-stage scheme of pornographic image detection is proposed in this paper. Specifically, we first apply the content-based image retrieval technique to find out whether human are present in the images. Then a detailed skin color analysis is performed to affirm the presence of pornographic content in the images. Experimental results show that the proposed algorithm performs well and fast in detecting pornographic images.

One of the challenges faced by surveillance video analysis is to detect objects from the frames. For outdoor surveillance, detection of small object like pedestrian is of particular interest. This paper proposes a fast, lightweight, and auto-zooming-based framework for small pedestrian detection. An attentive virtual auto-zooming scheme is proposed to adaptively zoom-in the input frame by splitting it into nonoverlapped tiles and pay attention to the only important tiles. Without sacrificing detection performance, we have obtained a fully convolutional pedestrian detection model which can be run on low computational resources. It has been trained on an outdoor surveillance dataset and evaluated on two specially prepared testing sets of small (far) pedestrians in outdoor surveillance. We have compared our framework performance with different single-step customized pedestrian detectors as well as the two-step detector faster R-CNN. The results validate the efficiency of our framework.INDEX TERMS Deep convolutional neural network, outdoor surveillance, real-time pedestrian detection, small pedestrian objects, virtual auto-zooming.

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