Airborne Video Research Articles

Method for airborne video capture and preprocessing based on FPGA

Method for airborne video capture and preprocessing based on FPGA

The Design of Real-Time and High-Speed Video Processing Platform for Aerial UAV Based on DM642

With the rapid development of aerial UAV (Unmanned Aerial Vehicle), the design of real-time data acquisition and transmission system for the video signal has a new applied field. It is different from traditional video acquisition and processing system, aerial video signal has the problems of screen jitter and spatial interference. The processing algorithm of aerial UAV airborne video signal is put forward in the paper, and the platform of high speed procession is constructed based on chip TMS320DM642, and get a good effect.

Automatic detection and tracking of multiple interacting targets from a moving platform

In real-world scenarios, a target tracking system could be severely compromised by interactions, i.e., influences from the proximity and/or behavior of other targets or background objects. Closely spaced targets are difficult to distinguish, and targets may be partially or totally invisible for uncontrolled durations when occluded by other objects. These situations are very likely to degrade the performance or cause the tracker to fail because the system may use invalid target observations to update the tracks. To address these issues, we propose an integrated multitarget tracking system. A background-subtraction–based method is used to automatically detect moving objects in video frames captured by a moving camera. The data association method evaluates the overlap rates between newly detected objects (observations) and already-tracked targets and makes decisions pertaining to whether a target is interacting with other targets and whether it has a valid observation. According to the association results, distinct strategies are employed to update and manage the tracks of interacting versus well-isolated targets. This system has been tested with real-world airborne videos from the DARPA Video Verification of Identity program database and demonstrated excellent track continuity in the presence of occlusions and multiple target interactions, very low false alarm rate, and real-time operation on an ordinary general-purpose computer.

Ego motion guided particle filter for vehicle tracking in airborne videos

Tracking in airborne circumstances is receiving more and more attention from researchers, and it has become one of the most important components in video surveillance for its advantage of better mobility, larger surveillance scope and so on. However, airborne vehicle tracking is very challenging due to the factors such as platform motion, scene complexity, etc. In this paper, to address these problems, a new framework based on Kanade–Lucas–Tomasi (KLT) features and particle filter is proposed. KLT features are tracked throughout the video sequence. At the beginning of video tracking, a strategy based on motion consistence with RANSAC is utilized to separate background KLT features. The grouping of background features helps estimate the ego motion of the platform and the estimation is then incorporated into the prediction step in particle filter. Color similarity and Hu moments are used in the measurement model to assign the weights of particles. Our experimental results demonstrated that the proposed method outperformed the other tracking methods.

A rapid and cost-effective method for vegetation mapping and nutrient content evaluation along the receding Lake Kinneret shoreline using oblique airborne video integrated into the <i>GeoSky</i><sup>™</sup> system

Lake Kinneret is a major water source for the State of Israel, hence it is managed and preserved by the Israel Water Authority. Mapping the new vegetation growth along the exposed shores of Lake Kinneret during periods of retreating lake water levels is of great importance in the management of the lake and its water quality. When the lake level rises this vegetation is covered by water, decays, and releases nutrients into the lake, possibly enhancing algal blooms. In December, 2001, when the lake level was -214.72 m, a rapid and cost-effective vegetation mapping was done along the retreating shoreline using a GeoSky ™ system that integrates oblique airborne video synchronized with Differential Global Positioning System (DGPS) and an electronic compass, and displays them within a Geographic Information System (GIS). Six major vegetation classes with twelve subclasses were interpreted from the video along the newly exposed shoreline between -209 m (highest lake level) and -214.72 m, their borders mapped, an...

REAL-TIME UAV BASED GEOSPATIAL VIDEO INTEGRATED INTO THE FIRE BRIGADES CRISIS MANAGEMENT GIS SYSTEM

Abstract. During a fire incident live airborne video offers the fire brigade an additional means of information. Essential for the effective usage of the daylight and infra red video data from the UAS is that the information is fully integrated into the crisis management system of the fire brigade. This is a GIS based system in which all relevant geospatial information is brought together and automatically distributed to all levels of the organisation. In the context of the Dutch Fire-Fly project a geospatial video server was integrated with a UAS and the fire brigades crisis management system, so that real-time geospatial airborne video and derived products can be made available at all levels during a fire incident. The most important elements of the system are the Delftdynamics Robot Helicopter, the Video Multiplexing System, the Keystone geospatial video server/editor and the Eagle and CCS-M crisis management systems. In discussion with the Security Region North East Gelderland user requirements and a concept of operation were defined, demonstrated and evaluated. This article describes the technical and operational approach and results.

Tracking vehicles as groups in airborne videos

Airborne vehicle tracking system is receiving increasing attention due to its high mobility, low cost and large surveillance scope. However, tracking multiple vehicles simultaneously on airborne platform is a challenging problem, owing to camera vibration, which causes visible frame-to-frame jitter in the airborne videos and uncertain vehicle motion. To address these problems, a new collaborative tracking framework is proposed in this paper. The framework consists of a two-level tracking process to track vehicles as groups. The higher level builds the relevance network and divides target vehicles into different groups, where the relevance is calculated based on the status information of vehicles obtained from the lower level. The proposed group tracking takes into account the relevance between vehicles and reduces the impact of camera vibration. Experimental results demonstrated that the proposed method has better performance in terms of tracking speed and tracking accuracy compared to other existing approaches based on particle filter and stationary grouping.

Detecting and Tracking Vehicles in Airborne Videos

Detecting and Tracking Vehicles in Airborne Videos

Fast motion detection from airborne videos using graphics processing unit

DCM Research Resources LLC, Germantown, Maryland 20874Abstract. In our previous work, we proposed a joint optical flow and principal componentanalysis (PCA) approach to improve the performance of optical flow based detection, wherePCA is applied on the calculated two-dimensional optical flow image, and motion detectionis accomplished by a metric derived from the two eigenvalues. To reduce the computationaltime when processing airborne videos, parallel computing using graphic processing unit(GPU) is implemented on NVIDIA GeForce GTX480. Experimental results demonstrate thatour approach can efficiently improve detection performance even with dynamic background,and processing time can be greatly reduced with parallel computing on GPU.

Visual Attention Accelerated Vehicle Detection in Low-Altitude Airborne Video of Urban Environment

One of the primary goals of the airborne vehicle detection system is to reduce the risks of incident collisions and to relieve traffic jam caused by the increasing number of vehicles. Different from the stationary systems, which are usually fixed on buildings, the airborne systems in unmanned aircrafts or satellites take the advantages of wider view angle and higher mobility. However, detecting vehicles in airborne videos is a challenging task because of the scene complexity and platform movement. The direct application of the traditional image processing techniques to the problem may result in low detection rate or cannot meet the requirements of real-time applications. To address these problems, a new and efficient method composed by two stages, attention focus extraction and vehicle classification is proposed in this paper. Our work makes two key contributions. The first is the introduction of a new attention focus extraction algorithm, which can quickly detect the candidate vehicle regions to make the algorithm focus on much smaller regions for faster computation. The second contribution is a simple and efficient classification process, which is built using the AdaBoost learning algorithm. The classification process, which is a hierarchical structure, is designed to obtain a lower false alarm rate by looking for vehicles in the candidate regions. Experimental results demonstrate that, compared with other representative algorithms, our method can obtain better performance in terms of higher detection rate and lower false positive rate, while meeting the needs of real-time application.

Estimation of a Road Mask to Improve Vehicle Detection and Tracking in Airborne Imagery

This research proposes a method to estimate a road mask in airborne imagery to improve vehicle detection and tracking. The road mask can remove false detections and reduce computation time. The road mask was estimated on the basis of smoothed (interpolated) positions of the vehicles tracked by the short-term and long-term trackers in the software tool “tracking and registration of airborne video image sequences” (TRAVIS) in a fixed number of frames (i.e., one cycle). First, raw vehicle positions that were obtained from TRAVIS were smoothed with a Kalman filter. Second, the slope of the roadway was estimated based on the smoothed positions of these vehicles. Third, falsely detected vehicles were removed if the slope of the lines that connected two vehicles in the same lane were close to the slope of the roadway. Finally, the road mask was generated according to the minimal and maximal intercepts of the approximated roadway edges in one cycle. Experimental results showed that the estimation of the road mask was accurate and that the improvement in the correct detection and tracking rate and in the computation time was significant.

Vehicle Detection and Motion Analysis in Low-Altitude Airborne Video Under Urban Environment

Visual surveillance from low-altitude airborne platforms plays a key role in urban traffic surveillance. Moving vehicle detection and motion analysis are very important for such a system. However, illumination variance, scene complexity, and platform motion make the tasks very challenging. In addition, the used algorithms have to be computationally efficient in order to be used on a real-time platform. To deal with these problems, a new framework for vehicle detection and motion analysis from low-altitude airborne videos is proposed. Our paper has two major contributions. First, to speed up feature extraction and to retain additional global features in different scales for higher classification accuracy, a boosting light and pyramid sampling histogram of oriented gradients feature extraction method is proposed. Second, to efficiently correlate vehicles across different frames for vehicle motion trajectories computation, a spatio-temporal appearance-related similarity measure is proposed. Compared to other representative existing methods, our experimental results showed that the proposed method is able to achieve better performance with higher detection rate, lower false positive rate, and faster detection speed.

Vehicle detection and tracking in airborne videos by multi-motion layer analysis

Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are receiving more and more attention due to their advantages of high mobility, fast deployment and large surveillance scope. However, such systems are difficult to develop because of factors like UAV motion, scene complexity, and especially the partial occlusion of targets. To address these problems, a new framework of multi-motion layer analysis is proposed to detect and track moving vehicles in airborne platform. After motion layers are constructed, they are maintained over time for tracking vehicles. Most importantly, since the vehicle motion layers can be maintained even when the vehicles are only partially observed, the proposed method is robust to partial occlusion. Our experimental results showed that (1) compared with other previous algorithms, our method can achieve better performance in terms of higher detection rate and lower false positive rate; (2) it is more efficient and more robust to partial occlusion; (3) it is able to meet the demand of real time application due to its computational simplicity.

A quick moving target detection method based on real-time airborne videos

In this paper, a fast detection method of moving target from real-time airborne videos is presented. We first explore the motion relationship in an airborne video and utilise the global motion estimation to estimate the background movement, and then use binocular interpolation for motion compensation; thus, the moving target detection in dynamic airborne videos is converted into detection in static backgrounds. A method involving the multiplication of the differences of the symmetrical images among three adjacent frames is adopted to enhance moving target and increase the difference between the target and background noises. The regional growth and morphological image processing techniques are employed finally to realise target segmentation. Experimental results demonstrate the effectiveness and robustness of our algorithm.

Geolocation of Multiple Targets from Airborne Video Without Terrain Data

The task of geolocating targets from airborne video is required for many applications in surveillance, law enforcement, reconnaissance, etc. The usual approaches to target geolocation involve terra...

Moving Object Verification in Airborne Video Sequences

This paper presents an end-to-end system for moving object verification in airborne video sequences. Using a sample selection module, the system first selects frames from a short sequence and stores them in an exemplar database. To handle appearance change due to potentially large aspect angle variations, a homography-based view synthesis method is then used to generate a novel view of each image in the exemplar database at the same pose as the testing object in each frame of a testing video segment. A rotationally invariant color matcher and a spatial-feature matcher based on distance transforms are combined using a weighted average rule to compare the novel view and the testing object. After looping over all testing frames, the set of match scores is passed to a temporal analysis module to examine the behavior of the testing object, and calculate a final likelihood. Very good verification performance is achieved over thousands of trials for both color and infrared video sequences using the proposed system.

Master of the Obscure—Automated Geostatistical Classification in Presence of Complex Geophysical Processes

The topic of this paper is the retrieval of hidden or secondary information on complex spatial variables from geophysical data. Typical situations of obscured geological or geophysical information are the following: (1) Noise may disturb the signal for a variable for which measurements have been collected. (2) The variable of interest may be obscured by other geophysical processes. (3) The information of interest may formally be captured in a secondary variable, whereas data may have been collected for a primary variable only, that is related to the geophysical process of interest. Examples discussed here include mapping of marine-geologic provinces from bathymetric data, identification of sea-ice properties from snow-depth data, analysis of snow surface data in an Alpine environment and association of deformation types in fast-moving glaciers from airborne video material or satellite imagery. Data types include geophysical profile or trackline data, image data, grid or matrix-type data, and more generally, any two-dimensional or three-dimensional discrete or discretizable data sets.

Airborne video for moving object detection, tracking, and segmentation

Airborne video for moving object detection, tracking, and segmentation

On the extraction of 3D models from airborne video sensors for geolocation

Geolocation of a feature in a video sequence collected from a moving platform is an activity that must be undertaken in video exploitation, especially surveillance and reconnaissance applications. Examples of sensor systems that are the focus of this work include manned and unmanned aerial vehicles. The approach described here uses positional information from three sources to compute refined coordinates in three dimensions for any feature in the video sequence. These three sources are: first, sensor-platform metadata describing the likely sensor footprint based on sensor-platform positional and attitudinal information; second, 3D information of a scene inherent in a video sequence collected from a moving platform; and third, reference imagery of the region of interest that is geolocated and georectified such as aerial photography. We describe the overall steps involved in this process and the progress made to date.

Background estimation under rapid gain change in thermal imagery

We consider detection of moving ground vehicles in airborne sequences recorded by a thermal sensor with automatic gain control, using an approach that integrates dense optic flow over time to maintain a model of background appearance and a foreground occlusion layer mask. However, the automatic gain control of the thermal sensor introduces rapid changes in intensity that makes this difficult. In this paper we show that an intensity-clipped affine model of sensor gain is sufficient to describe the behavior of our thermal sensor. We develop a method for gain estimation and compensation that uses sparse flow of corner features to compute the affine background scene motion that brings pairs of frames into alignment prior to estimating change in pixel brightness. Dense optic flow and background appearance modeling is then performed on these motion-compensated and brightness-compensated frames. Experimental results demonstrate that the resulting algorithm can segment ground vehicles from thermal airborne video while building a mosaic of the background layer, despite the presence of rapid gain changes.