Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

Liu, Weizhe; Lis, Krzysztof; Salzmann, Mathieu; Fua, Pascal

doi:10.48550/arxiv.1803.08805

Cited by 3 publications

(5 citation statements)

References 24 publications

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“…We have presented a complete pipeline for removing perspective distortion from an image, and obtaining the bird's eye view from a monocular image automatically. Our method can be used as plug and play to help other networks which suffer from multiple-scales due to perspective distortion such as vehicle tracking [28], crowd counting [24,25] or penguin counting [4] etc. Our method is fast, robust and can be used in real-time on videos to generate a bird's eye view of the scene.…”

Section: Discussionmentioning

confidence: 99%

“…It can be used as a preprocessing step for many other computer vision tasks like object detection [19,29] and tracking [10], and has applications in video surveillance and traffic control. For example, in crowd counting, where perspective distortion affects the crowd density in the image, the crowd density can instead be predicted in the world [24].…”

Section: Introductionmentioning

confidence: 99%

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A Geometric Approach to Obtain a Bird's Eye View From an Image

Abbas

Zisserman

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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The objective of this paper is to rectify any monocular image by computing a homography matrix that transforms it to a bird's eye (overhead) view.We make the following contributions: (i) we show that the homography matrix can be parameterised with only four parameters that specify the horizon line and the vertical vanishing point, or only two if the field of view or focal length is known; (ii) We introduce a novel representation for the geometry of a line or point (which can be at infinity) that is suitable for regression with a convolutional neural network (CNN); (iii) We introduce a large synthetic image dataset with ground truth for the orthogonal vanishing points, that can be used for training a CNN to predict these geometric entities; and finally (iv) We achieve state-of-theart results on horizon detection, with 74.52% AUC on the Horizon Lines in the Wild dataset. Our method is fast and robust, and can be used to remove perspective distortion from videos in real time.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Geometric Approach to Obtain a Bird's Eye View From an Image

Abbas

Zisserman

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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“…Geometry-Aware Deep Learning. Scene geometry is considered as important prior information for computer vision tasks [21,37]. Leibe et al [18] explored joint object tracking and detection using geometry assumptions within a traditional non-deep-learning framework.…”

Section: Related Workmentioning

confidence: 99%

“…Leibe et al [18] explored joint object tracking and detection using geometry assumptions within a traditional non-deep-learning framework. In crowd counting [21], as the camera usually sits on a fixed position and the variance between people's height is small, it is easy to obtain the homography between the image and the head plane. By incorporating this information in the model, it becomes possible to directly predict the crowd density in the physical world.…”

Section: Related Workmentioning

confidence: 99%

Geometry-Aware Video Object Detection for Static Cameras

Xu,

Xie,

Zisserman

2019

Preprint

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In this paper we propose a geometry-aware model for video object detection. Specifically, we consider the setting that cameras can be well approximated as static, e.g. in video surveillance scenarios, and scene pseudo depth maps can therefore be inferred easily from the object scale on the image plane.We make the following contributions: First, we extend the recent anchor-free detector (CornerNet [17]) to video object detections. In order to exploit the spatial-temporal information while maintaining high efficiency, the proposed model accepts video clips as input, and only makes predictions for the starting and the ending frames, i.e. heatmaps of object bounding box corners and the corresponding embeddings for grouping. Second, to tackle the challenge from scale variations in object detection, scene geometry information, e.g. derived depth maps, is explicitly incorporated into deep networks for multiscale feature selection and for the network prediction. Third, we validate the proposed architectures on an autonomous driving dataset generated from the Carla simulator [5], and on a real dataset for human detection (DukeMTMC dataset [28]). When comparing with the existing competitive single-stage or two-stage detectors, the proposed geometryaware spatio-temporal network achieves significantly better results.

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“…A lot of work has been proposed to improve the performance of detection algorithms. These studies either focus on proposing more advanced network structures (for example multi-column network [1,2], scale aggregation module [3,4] and scale adaptive module [5,6,7]), or focus on designing more suitable loss functions [8]. These two focus points have greatly improved the performance of existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

Enhancing and Dissecting Crowd Counting By Synthetic Data

Hou¹,

Li²,

Lu³

et al. 2022

Preprint

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In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity. The experimental results of using this dataset as data enhancement show that the performance of the proposed streamlined and efficient benchmark network ESA-Net can be improved by 8.4%. The other two classic heterogeneous architectures MCNN and CSRNet pre-trained on CrowdX also show significant performance improvements. Considering many influencing factors determine performance, such as background, camera angle, human density, and resolution. Although these factors are important, there is still a lack of research on how they affect crowd counting. Thanks to the CrowdX dataset with rich annotation information, we conduct a large number of data-driven comparative experiments to analyze these factors. Our research provides a reference for a deeper understanding of the crowd counting problem and puts forward some useful suggestions in the actual deployment of the algorithm.

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Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

Cited by 3 publications

References 24 publications

A Geometric Approach to Obtain a Bird's Eye View From an Image

A Geometric Approach to Obtain a Bird's Eye View From an Image

Geometry-Aware Video Object Detection for Static Cameras

Enhancing and Dissecting Crowd Counting By Synthetic Data

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