Using Telemetry Data for Video Compression on Unmanned Air Vehicles

Abstract

Unmanned Aerial Vehicles in the "mini" to "micro" size ranges (<6 foot wingspan) are becoming increasingly popular platforms for the collection of video data about an area of interest. Currently, mini-UAV platforms transmit video back to the end user using analog RF transmitters. Transmission of digital video, although it is more desirable on a mini-UAV platform, requires video compression to be performed. Unfortunately, determining the motion information required for digital video compression is a computationally expensive procedure. Hardware capable of performing this task is typically too heavy, too large, and too power intensive to be carried on a mini-UAV.We present an efficient algorithm for solving the motion estimation problem which requires far less computation, making it feasible for implementation on-board a mini-UAV. We reduce the amount of computation necessary by using (1) knowledge of camera locations (from available mini-UAV sensor data) and (2) the projective geometry of the camera. We present encouraging initial results, showing that this method reduces computations by almost two orders of magnitude compared to standard methods, and yields comparable or improved compression results.

Vision-Based Tracking and Motion Estimation for Moving Targets Using Unmanned Air Vehicles

“…Another involves a swarm of micro UAVs deployed from a mother ship that can provide imagery of the hidden targets. However, because each approach uses limited throughput wireless communication to deliver compressed video [24] and telemetry to the human operator, the tracking loss events are unavoidable and must be explicitly addressed. This issue has been central to the design of the particle filter reported in [6].…”

Metadata-Assisted Global Motion Estimation for Medium-Altitude Unmanned Aerial Vehicle Video Applications

“…Rodrí guez et al [35] presented an efficient algorithm to solve the motion estimation problem. The algorithm requires minimal computation and is thus suitable for implementation in a mini-UAV.…”

“…Computation is reduced by using prior knowledge on camera locations (from available mini-UAV sensor data) and projective geometry of the camera. Based on the algorithm in [35], Gong et al [36] proposed a low-complexity image-sequence compressing algorithm for UAVs. Bhaskaranand et al [37] designed a video encoding scheme suitable for applications in which encoder complexity needs to be low (e.g., UAV video surveillance).…”

“…Thus, all pixels of an image are assumed to be on the same plane. Field depth consistency is a basic hypothesis in most studies on GME [35][36][37][38][39]. The lower the terrain fluctuations and buildings are, the more accurate the result of GME is.…”

Vision-Based Tracking and Motion Estimation for Moving Targets Using Small UAVs

“…The "measurement" of the target is assumed to be constantly at the center of the image frame therefore significantly 24 and telemetry to the human operator the tracking loss events are unavoidable and must be explicitly addressed. This issue has been central to the design of the particle filter reported in Ref.…”