Detecting and tracking all moving objects in wide-area aerial video

Pollard, T.B.; Antone, Matthew

doi:10.1109/cvprw.2012.6239201

Cited by 45 publications

(24 citation statements)

References 9 publications

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“…A comprehensive framework for tracking was presented in [10], where velocity and appearance models were taken into consideration for tracking objects in CLIF data. However, the appearance model used in [10] is the squared difference of the intensities of the object region. This measure would be distorted in regions where there is variation in illumination and when the object is rotated.…”

Section: Related Workmentioning

confidence: 99%

Tracking in Wide Area Motion Imagery Using Phase Vector Fields

Santhaseelan

Asari

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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Tracking in wide area motion imagery (WAMI) is extremely complex because of low resolution of targets, low frame rate and a host of other detrimental environmental factors. In this paper, we propose a robust feature-based method to track objects in WAMI data by exploiting local phase and orientation information based on the monogenic signal representation. We present a detailed monogenic space based analysis to develop a robust method to track objects of low resolution in wide area aerial surveillance imagery. By exploiting local phase information at multiple scales, objects in shadows can be tracked. We also propose an efficient technique to make the feature representation of objects rotation invariant by utilizing local orientation of the object region. The proposed technique is shown to be robust in challenging situations that are characteristic of wide area motion imagery. Effectiveness of the proposed method is illustrated by comparing its performance with dense set of SIFT features and mean shift tracker.

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Section: Related Workmentioning

confidence: 99%

Tracking in Wide Area Motion Imagery Using Phase Vector Fields

Santhaseelan

Asari

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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“…This approach is based on a non-symmetrical distance measure which is common in the context of document analysis (Gesù and Starovoitov, 1999) and (Baudrier et al, 2008). Application to background subtraction is used in (Pollard and Antone, 2012) while (Saur and Krüger, 2012) and (Saur et al, 2014) are applying it for detecting changes in video image pairs.…”

Section: Algorithms For Detecting Changesmentioning

confidence: 99%

Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing

Saur

Krüger

2016

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

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ABSTRACT:Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label "new object" is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label "vanished object" corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two "directed" change masks and differs from image differencing where only one "undirected" change mask is extracted which combines both label types to the single label "changed object". The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.

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“…Many techniques for object tracking [1,2] and inferring inter-camera relationships [3] from stationary sensor networks have been developed to exploit these systems; however, utilization of these techniques to survey a particular object or event requires that the camera system be deployed in the area and at the time of interest, which in turn requires a-priori knowledge of the event.…”

Section: Introductionmentioning

confidence: 99%

Grouping Crowd-Sourced Mobile Videos for Cross-Camera Tracking

Frey

Antone²

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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Public adoption of camera-equipped mobile phones has given the average observer of an event the ability to capture their perspective and upload the video for online viewing (e.g. YouTube). When traditional wide-area surveillance systems fail to capture an area or time of interest, crowd-sourced videos can provide the information needed for event reconstruction. This paper presents the first end-to-end method for automatic cross-camera tracking from crowd-sourced mobile video data.Our processing (1) sorts videos into overlapping space-time groups, (2) finds the inter-camera relationships from objects within each view, and (3) provides an end user with multiple stabilized views of tracked objects. We demonstrate the system's effectiveness on a real dataset collected from YouTube.

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Detecting and tracking all moving objects in wide-area aerial video

Cited by 45 publications

References 9 publications

Tracking in Wide Area Motion Imagery Using Phase Vector Fields

Tracking in Wide Area Motion Imagery Using Phase Vector Fields

Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing

Grouping Crowd-Sourced Mobile Videos for Cross-Camera Tracking

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