Autonomous Searching and Tracking of a River using an UAV

Abstract—Surveillance operations include inspecting and monitoring river boundaries, bridges and coastlines. An au-tonomous Unmanned Aerial Vehicle (UAV) can decrease the operational costs, expedite the monitoring process and be used in situations where a manned inspection is not possible. This paper addresses the problem of searching and mapping such littoral boundaries using an autonomous UAV based on visual feedback. Specifically, this paper describes an exploration system that equips a fixed wing UAV to autonomously search a given area for a specified structure (could be a river, a coastal line etc.), identify the structure if present and map the coordinates of the structure based on the images from the onboard sensor(could be vision or near infra-red). Experimental results with a fixed wing UAV searching and mapping the coordinates of a 2 mile stretch of a river with a cross track error of around 9 meters are presented. I

Geometry of Vanishing Points and its Application to External Calibration and Realtime Pose Estimation

Vanishing points of an image contain important information for camera calibration. Various calibration techniques have been introduced using the properties of vanishing points to find intrinsic and extrinsic calibration parameters. This paper revisits the vanishing points geometry and suggests a simple extrinsic parameter estimation algorithm which uses a single rectangle. The comparison with the Camera Calibration Toolbox for Matlab ® shows that the proposed algorithm is highly competitive. The suggested technique is also applied to a realtime pose estimation for an unmanned air vehicle’s navigation in an urban environment. We present a realtime vanishing point extraction algorithm and a pose estimation procedure. The experimental result on a real flight video clip is presented

Realtime object tracking based on dynamic feature grouping with background subtraction

Object detection and tracking has various application areas including intelligent transportation systems. We introduce an object detection and tracking approach that combines the background subtraction algorithm and the feature tracking and grouping algorithm. We first present an augmented background subtraction algorithm which uses a low-level feature tracking as a cue. The resulting background subtraction cues are used to improve the feature detection and grouping result. We then present a dynamic multi-level feature grouping approach that can be used in real time applications and also provides high-quality trajectories. Experimental results from video clips of a challenging transportation application are presented. 1. Introduction and Previou

Geometry of Vanishing Points and its Application to External Calibration and Realtime Pose Estimation

Vanishing points of an image contain important information for camera calibration. Various calibration techniques have been introduced using the properties of vanishing points to find intrinsic and extrinsic calibration parameters. This paper revisits the vanishing points geometry and suggests a simple extrinsic parameter estimation algorithm which uses a single rectangle. The comparison with the Camera Calibration Toolbox for Matlab ® shows that the proposed algorithm is highly competitive. The suggested technique is also applied to a realtime pose estimation for an unmanned air vehicle’s navigation in an urban environment. We present a realtime vanishing point extraction algorithm and a pose estimation procedure. The experimental result on a real flight video clip is presented.Air Transportation

Robust lane detection and tracking in challenging scenarios

A lane-detection system is an important component of many intelligent transportation systems. We present a robust lane-detection-and-tracking algorithm to deal with challenging scenarios such as a lane curvature, worn lane markings, lane changes, and emerging, ending, merging, and splitting lanes. We first present a comparative study to find a good real-time lane-marking classifier. Once detection is done, the lane markings are grouped into lane-boundary hypotheses. We group left and right lane boundaries separately to effectively handle merging and splitting lanes. A fast and robust algorithm, based on random-sample consensus and particle filtering, is proposed to generate a large number of hypotheses in real time. The generated hypotheses are evaluated and grouped based on a probabilistic framework. The suggested framework effectively combines a likelihood-based object-recognition algorithm with a Markov-style process (tracking) and can also be applied to general-part-based object-tracking problems. An experimental result on local streets and highways shows that the suggested algorithm is very reliable

Multi-Sensor Traffic Data Fusion

This report describes unique surveillance system on a section of I-80 freeway in the city of Emeryville. The system, called the Berkeley Highway Laboratory (BHL), consists of eight dual loop detector stations along the freeway section, and 12 video cameras. Advanced machine vision algorithms were developed to process the video data to generate vehicle trajectories. Efforts are underway to fuse the loop and video detector data to obtain detailed and accurate information on traffic operating condition

Multi-Sensor Traffic Data Fusion

This report describes unique surveillance system on a section of I-80 freeway in the city of Emeryville. The system, called the Berkeley Highway Laboratory (BHL), consists of eight dual loop detector stations along the freeway section, and 12 video cameras. Advanced machine vision algorithms were developed to process the video data to generate vehicle trajectories. Efforts are underway to fuse the loop and video detector data to obtain detailed and accurate information on traffic operating conditions

Pseudoreal-time activity detection for railroad grade-crossing safety

It is important to understand the factors underlying gradecrossing crashes and to examine potential solutions. We have installed a camera in front of a locomotive to examine grade-crossing accidents (or near accidents). We present a computer vision system that automatically extracts possible near-accident scenes by detecting the activity of vehicles crossing in front of the train after signals are ignited. We present a fast algorithm to detect moving objects recorded by a moving camera with minimal computation. The moving object is detected by: 1) estimating the ego motion of the camera and 2) detecting and tracking feature points whose motion is inconsistent with the camera motion. We introduce a pseudoreal-time ego-motion (camera-motion) estimation method with a robust optimization algorithm. We present experiments on ego-motion estimation and moving-object detection. Our algorithm works in pseudoreal-time and we expect that our algorithm can be applied to real-time applications such as collision warning in the near future, with the development of hardware technology