From images to traffic behavior - A UAV tracking and monitoring application

Heintz, Fredrik; Rudol, Piotr; Doherty, Patrick

doi:10.1109/icif.2007.4408103

Cited by 61 publications

(40 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Concrete events corresponding to changes in such attributes and predicates finally provide sufficient information for the chronicle recognition system to determine when higher-level events such as reckless overtakes occur. Details about the image processing and the recognition of traffic violations can be found in [13].…”

Section: A Traffic Monitoring Scenariomentioning

confidence: 99%

A stream-based hierarchical anchoring framework

Heintz

Kvarnström

Doherty

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

View full text Add to dashboard Cite

Abstract-Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols labeling physical objects and the sensor data being collected about them, a process called anchoring.In this paper we present a stream-based hierarchical anchoring framework extending the DyKnow knowledge processing middleware. A classification hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of classification, permitting symbolic reasoning on different levels of abstraction. The approach has been applied to a traffic monitoring application where an unmanned aerial vehicle collects information about a small urban area in order to detect traffic violations.

show abstract

Section: A Traffic Monitoring Scenariomentioning

confidence: 99%

A stream-based hierarchical anchoring framework

Heintz

Kvarnström

Doherty

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Their ability and suitability in performing dangerous and repetitive tasks as well as providing high spatial and temporal resolution imagery are great advantages that have made such a spread use of this technology (Rango, et al, 2006;Heintz, et al, 2007;Semsch et al, 2009;Remondino et al, 2011;Saari, et al, 2011;Neitzel et al, 2011;Nex et al, 2014;BollardBreen et al, 2015;Wischounig-Strucl and Rinner, 2015). In most of these applications it is essential to determine the exterior orientation of sensor and precise ground locations of UAV images.…”

Section: Introductionmentioning

confidence: 99%

Precise 3d Geo-Location of Uav Images Using Geo-Referenced Data

Hamidi

Samadzadegan

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Commission VI, WG VI/4 KEY WORDS: UAV, Geo-location, Direct Geo-referencing, Database matching, Ray-DSM intersection. ABSTRACT:In most of UAV applications it is essential to determine the exterior orientation of on-board sensors and precise ground locations of images acquired by them. This paper presents a precise methodology for 3D geo-location of UAV images using geo-referenced data. The fundamental concept behind this geo-location process is using database matching technique for refining the coarse initial attitude and position parameters of the camera derived from the navigation data. These refined exterior orientation parameters are then used for geo-locating entire image frame using rigorous collinearity model in a backward scheme. A forwards geo-locating procedure also is proposed based on a ray-DSM intersection method for the cases where ground location of specific image targets (and not the entire frame) is required. Experimental results demonstrated the potential of the proposed method in accurate geo-location of UAV images. Applying this method, an RMSE of about 14 m in horizontal and 3D positions has been obtained.

show abstract

“…Examples are military persistent wide-area aerial surveillance [1], mapping image sequences onto maps [10], tracking cars for traffic monitoring [9], long-term tracking of targets by a swarm [11], and detection of human activities in the video streams [3]. Before we discuss the novelty of this paper, we will first motivate which insights and components we will adopt from the research community.…”

Section: Related Workmentioning

confidence: 99%

Focus-of-attention for human activity recognition from UAVs

2014

View full text Add to dashboard Cite

This paper presents a system to extract metadata about human activities from full-motion video recorded from a UAV. The pipeline consists of these components: tracking, motion features, representation of the tracks in terms of their motion features, and classification of each track as one of the human activities of interest. We consider these activities: walk, run, throw, dig, wave. Our contribution is that we show how a robust system can be constructed for human activity recognition from UAVs, and that focus-of-attention is needed. We find that tracking and human detection are essential for robust human activity recognition from UAVs. Without tracking, the human activity recognition deteriorates. The combination of tracking and human detection is needed to focus the attention on the relevant tracks. The best performing system includes tracking, human detection and a per-track analysis of the five human activities. This system achieves an average accuracy of 93%. A graphical user interface is proposed to aid the operator or analyst during the task of retrieving the relevant parts of video that contain particular human activities. Our demo is available on YouTube.

show abstract

From images to traffic behavior - A UAV tracking and monitoring application

Cited by 61 publications

References 9 publications

A stream-based hierarchical anchoring framework

A stream-based hierarchical anchoring framework

Precise 3d Geo-Location of Uav Images Using Geo-Referenced Data

Focus-of-attention for human activity recognition from UAVs

Contact Info

Product

Resources

About