Vehicle detection and tracking in wide field-of-view aerial video

Xiao, Jiangjian; Cheng, Hui; Sawhney, Harpreet; Han, Feng

doi:10.1109/cvpr.2010.5540151

Cited by 119 publications

(87 citation statements)

References 10 publications

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“…This technique tracks very robustly objects from aerial views [45], and in our case, people who are close to a pure overhead view. Side views are more difficult for background subtraction, especially with traffic in the background and significant occlusion between pedestrians.…”

Section: Preprocessingmentioning

confidence: 99%

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3D skeletal reconstruction from low-resolution multi-view images

Taylor

Spiro

Bregler

2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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This paper demonstrates how 3D skeletal reconstruction can be performed by using a pose-sensitive embedding technique applied to multi-view video recordings. We apply our approach to challenging low-resolution video sequences. Usually skeletal reconstruction can be only achieved with many calibrated high-resolution cameras, and only blob detection can be achieved with such low-resolution imagery. We show that with this embedding technique (a metric learning technique using a deep convolutional architecture), we achieve very good 3D skeletal reconstruction on low-resolution outdoor scenes with many challenges.

show abstract

Section: Preprocessingmentioning

confidence: 99%

“…We first located people on the scene by running on the overhead views an adaptive background subtraction technique (figure 2) followed by a graph matching-based blob tracking [45,48] technique. This technique tracks very robustly objects from aerial views [45], and in our case, people who are close to a pure overhead view.…”

Section: Preprocessingmentioning

confidence: 99%

3D skeletal reconstruction from low-resolution multi-view images

Taylor

Spiro

Bregler

2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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show abstract

“…Reilly et al detected moving objects by median background model, and objects are tracked using bipartite graph matching with the combination of road orientation and object context [12]. Xiao et al proposed a joint probabilistic relation graph approach to track a large number of vehicles [17]. This method utilized vehicle behavior model from road structure to detect and track in wide area.…”

Section: Previous Workmentioning

confidence: 99%

Recognizing Humans in Motion: Trajectory-based Aerial Video Analysis

Iwashita

Ryoo

Fuchs³

et al. 2013

Procedings of the British Machine Vision Conference 2013

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We propose a novel method for recognizing people in aerial surveillance videos. Aerial surveillance images cover a wide area at low resolution. In order to detect objects (e.g., pedestrians) from such videos, conventional methods either utilize appearance information from raw videos or extract blob information from background subtraction results. However, people seen in low resolution images have less appearance information, and hence are very difficulty to classify based on their appearance or blob size. In addition, due to heavy camera movements caused by aerial vehicle ego-motion and wind, the system is expected to generate many noisy false detections including parallax. The idea presented in this paper is to detect and classify objects from aerial videos based on their motion: we analyze a trajectory of each object candidate, deciding whether it is a person-of-interest or simple noise based on how it moved. After objects are tracked by a Kalman filter-based tracking, we represent their motion as multi-scale histograms of 'orientation changes', which efficiently captures movements displayed by objects. Random forest classifiers are applied to our new representation to make the decision. The experimental results illustrate that our approach recognizes objects-of-interest (i.e., humans) even when there exist a large number of false detection/tracking, and it does it more reliably compared to the approaches with previous paradigm.

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“…In contrast to the above mentioned approaches, which take information from a single image only, Truemer et al (Tuermer et al, 2011) tried to enhance the performance by incorporating temporal information from motion analysis into the detection process. In (Xiao et al, 2010), the authors also employed a motion analysis by a three-frame subtraction scheme; moreover, they proposed a method for track associations by graph matching and vehicle behaviour modelling. Next to the region or sliding window based method in (Cheng et al, 2012), they also designed a pixel-wise detector of vehicles which employs dynamic Bayesian network in the classification step.…”

Section: Related Workmentioning

confidence: 99%

“…Current approaches in vehicle tracking from aerial or satellite imagery aim at off-line optimization of data association, e.g. by deploying bipartite graph matching (Xiao et al, 2010, Reilly et al, 2010 or by revising temporal tracking correspondence as done by Saleemi and Shah in (Saleemi and Shah, 2013) by maintaining multiple possible candidate tracks per object using a context-aware association (vehicle leading model, avoidance of track intersection) and applying a weighted hypothetical measurement derived from the observed measurement distribution.…”

Section: Related Workmentioning

confidence: 99%

Automatic Vehicle Trajectory Extraction for Traffic Analysis From Aerial Video Data

Apeltauer

Babinec²,

Herman³

et al. 2015

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

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ABSTRACT:This paper presents a new approach to simultaneous detection and tracking of vehicles moving through an intersection in aerial images acquired by an unmanned aerial vehicle (UAV). Detailed analysis of spatial and temporal utilization of an intersection is an important step for its design evaluation and further traffic inspection. Traffic flow at intersections is typically very dynamic and requires continuous and accurate monitoring systems. Conventional traffic surveillance relies on a set of fixed cameras or other detectors, requiring a high density of the said devices in order to monitor the intersection in its entirety and to provide data in sufficient quality. Alternatively, a UAV can be converted to a very agile and responsive mobile sensing platform for data collection from such large scenes. However, manual vehicle annotation in aerial images would involve tremendous effort. In this paper, the proposed combination of vehicle detection and tracking aims to tackle the problem of automatic traffic analysis at an intersection from visual data. The presented method has been evaluated in several real-life scenarios.

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Vehicle detection and tracking in wide field-of-view aerial video

Cited by 119 publications

References 10 publications

3D skeletal reconstruction from low-resolution multi-view images

3D skeletal reconstruction from low-resolution multi-view images

Recognizing Humans in Motion: Trajectory-based Aerial Video Analysis

Automatic Vehicle Trajectory Extraction for Traffic Analysis From Aerial Video Data

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