3D Urban Reconstruction from Wide Area Aerial Surveillance Video

Kang, Zhuoliang; Medioni, Gérard

doi:10.1109/wacvw.2015.17

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…With current sensor and storage technologies, WAMI is typically captured in large format (tens ∼ hundreds of megapixels), low frame rate (1 ∼ 2 Hz), grayscale, and with ground sampling distance from 0.2 ∼ 0.5 meter/pixel. Because of these unique characteristics and its wide applications, it has gained attention in the computer vision field in recent years [1,2,3,4,5,6,7]. Tracking many vehicles in WAMI [6,7,8,9,10] is an essential component since it is needed for higher level activity analysis and scene understanding.…”

Section: Introductionmentioning

confidence: 99%

Exploring Local Context for Multi-target Tracking in Wide Area Aerial Surveillance

Chen

Medioni

2017

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Abstract. Tracking many vehicles in wide coverage aerial imagery is crucial for understanding events in a large field of view. Most approaches aim to associate detections from frame differencing into tracks. However, slow or stopped vehicles result in long-term missing detections and further cause tracking discontinuities. Relying merely on appearance clue to recover missing detections is difficult as targets are extremely small and in grayscale. In this paper, we address the limitations of detection association methods by coupling it with a local context tracker (LCT), which does not rely on motion detections. On one hand, our LCT learns neighboring spatial relation and tracks each target in consecutive frames using graph optimization. It takes the advantage of context constraints to avoid drifting to nearby targets. We generate hypotheses from sparse and dense flow efficiently to keep solutions tractable. On the other hand, we use detection association strategy to extract short tracks in batch processing. We explicitly handle merged detections by generating additional hypotheses from them. Our evaluation on wide area aerial imagery sequences shows significant improvement over state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Exploring Local Context for Multi-target Tracking in Wide Area Aerial Surveillance

Chen

Medioni

2017

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

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“…This assumption is often broken in urban scenarios, in which there are many untextured or poorly illuminated regions that challenge existing multiview stereo approaches [8,11,17].…”

Section: Dense 3d Reconstructionmentioning

confidence: 99%

Street-View Change Detection with Deconvolutional Networks

Alcantarilla¹,

Stent²,

Ros³

et al.

Robotics: Science and Systems XII

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Abstract-We propose a system for performing structural change detection in street-view videos captured by a vehiclemounted monocular camera over time. Our approach is motivated by the need for more frequent and efficient updates in the large-scale maps used in autonomous vehicle navigation. Our method chains a multi-sensor fusion SLAM and fast dense 3D reconstruction pipeline, which provide coarsely registered image pairs to a deep deconvolutional network for pixel-wise change detection. To train and evaluate our network we introduce a new urban change detection dataset which is an order of magnitude larger than existing datasets and contains challenging changes due to seasonal and lighting variations. Our method outperforms existing literature on this dataset, which we make available to the community, and an existing panoramic change detection dataset, demonstrating its wide applicability.

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