We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape. The instructions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottom-up techniques for this shape parsing task rely on primitive detection and are inherently slow since the search space over possible primitive combinations is large. In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions. Our model is also more effective as a shape detector compared to existing state-of-the-art detection techniques. We finally demonstrate that our network can be trained on novel datasets without ground-truth program annotations through policy gradient techniques.
Persistent Aerial Tracking system for UAVs
In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
The ability to capture stabilized high resolution video from low-cost UAVs has the potential to significantly redefine future objectives in the development of state-of-the-art object tracking methods. In this paper, we propose a persistent, robust and autonomous object tracking system designed for UAV applications, called Persistent Aerial Tracking (PAT) (see Fig. 1). Persistent aerial tracking can serve many purposes, not only related to surveillance but also search and rescue, wild-life monitoring, crowd monitoring/management, and extreme sports. Deploying PAT on UAVs is a very promising application, since the camera can follow the target based on its visual feedback and actively change its orientation and position to optimize for tracking performance (e.g. persistent tracking accuracy in the presence of occlusion or fast motion across large and diverse areas). This is the defining difference with static tracking systems, which passively analyze a dynamic scene to produce analytics for other systems. It enables ad-hoc and low-cost surveillance that can be quickly deployed, especially in locales where surveillance infrastructure is not already established or feasible (e.g. remote locations, rugged terrain, and large water bodies.