Fully Automatic Roadside Camera Calibration for Traffic Surveillance

Dubská, Markéta; Herout, Adam; Juránek, Roman; Sochor, Jakub

doi:10.1109/tits.2014.2352854

Cited by 92 publications

(64 citation statements)

References 21 publications

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“…Many applications, however, require automatic calibration. In specific cases, it is possible to exploit geometric image elements such as lines [7], vanishing points [40], or circles [5] to compute the intrinsics, but these methods do not generalize to arbitrary natural images.…”

Section: Focal Length Estimationmentioning

confidence: 99%

GP2C: Geometric Projection Parameter Consensus for Joint 3D Pose and Focal Length Estimation in the Wild

Grabner

Roth

Lepetit

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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We present a joint 3D pose and focal length estimation approach for object categories in the wild. In contrast to previous methods that predict 3D poses independently of the focal length or assume a constant focal length, we explicitly estimate and integrate the focal length into the 3D pose estimation. For this purpose, we combine deep learning techniques and geometric algorithms in a two-stage approach: First, we estimate an initial focal length and establish 2D-3D correspondences from a single RGB image using a deep network. Second, we recover 3D poses and refine the focal length by minimizing the reprojection error of the predicted correspondences. In this way, we exploit the geometric prior given by the focal length for 3D pose estimation. This results in two advantages: First, we achieve significantly improved 3D translation and 3D pose accuracy compared to existing methods. Second, our approach finds a geometric consensus between the individual projection parameters, which is required for precise 2D-3D alignment. We evaluate our proposed approach on three challenging real-world datasets (Pix3D, Comp, and Stanford) with different object categories and significantly outperform the state-of-the-art by up to 20% absolute in multiple different metrics.

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Section: Focal Length Estimationmentioning

confidence: 99%

GP2C: Geometric Projection Parameter Consensus for Joint 3D Pose and Focal Length Estimation in the Wild

Grabner

Roth

Lepetit

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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“…Then we can derive two vanishing points with a method [9] based on the least square error. With 2 vanishing points u, v, we can get K, R, t according to [10].…”

Section: B 3d Projectionmentioning

confidence: 99%

Training-free Monocular 3D Event Detection System for Traffic Surveillance

Chen

Liu

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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We focus on the problem of detecting traffic events in a surveillance scenario, including the detection of both vehicle actions and traffic collisions. Existing event detection systems are mostly learning-based and have achieved convincing performance when a large amount of training data is available. However, in real-world scenarios, collecting sufficient labeled training data is expensive and sometimes impossible (e.g. for traffic collision detection). Moreover, the conventional 2D representation of surveillance views is easily affected by occlusions and different camera views in nature. To deal with the aforementioned problems, in this paper, we propose a training-free monocular 3D event detection system for traffic surveillance. Our system firstly projects the vehicles into the 3D Euclidean space and estimates their kinematic states. Then we develop multiple simple yet effective ways to identify the events based on the kinematic patterns, which need no further training. Consequently, our system is robust to the occlusions and the viewpoint changes. Exclusive experiments report the superior result of our method on large-scale real-world surveillance datasets, which validates the effectiveness of our proposed system. The demonstration videos of our system are available online 1 .

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“…Different methods may require various forms of user inputs, such as drawing parallel lines [12], camera position [22,14], and average vehicle size [3] or speed [16]. Fully automatic calibration is also achievable according to [4,18].…”

Section: Related Workmentioning

confidence: 99%

Traffic Danger Recognition With Surveillance Cameras Without Training Data

Zhang²,

Chen

et al. 2018

2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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We propose a traffic danger recognition model that works with arbitrary traffic surveillance cameras to identify and predict car crashes. There are too many cameras to monitor manually. Therefore, we developed a model to predict and identify car crashes from surveillance cameras based on a 3D reconstruction of the road plane and prediction of trajectories. For normal traffic, it supports realtime proactive safety checks of speeds and distances between vehicles to provide insights about possible high-risk areas. We achieve good prediction and recognition of car crashes without using any labeled training data of crashes. Experiments on the BrnoCompSpeed dataset show that our model can accurately monitor the road, with mean errors of 1.80% for distance measurement, 2.77 km/h for speed measurement, 0.24 m for car position prediction, and 2.53 km/h for speed prediction.

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Fully Automatic Roadside Camera Calibration for Traffic Surveillance

Cited by 92 publications

References 21 publications

GP2C: Geometric Projection Parameter Consensus for Joint 3D Pose and Focal Length Estimation in the Wild

GP2C: Geometric Projection Parameter Consensus for Joint 3D Pose and Focal Length Estimation in the Wild

Training-free Monocular 3D Event Detection System for Traffic Surveillance

Traffic Danger Recognition With Surveillance Cameras Without Training Data

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