A method of perspective normalization for video images based on map data

Lin, Bingxian; Xu, Changlu; Lan, Xin; Zhou, Liangchen

doi:10.1080/19475683.2019.1704870

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…A nonlinear perspective correction model has been used to calculate a holography matrix based on multiple matching points to implement the geospatial mapping of video objects (Zhang et al, 2021). This method has the advantages of facile parameter acquisition and convenient calculation (Lin et al, 2020a).…”

Section: Related Studiesmentioning

confidence: 99%

Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos

Liu

Han

Song

et al. 2023

Transactions in GIS

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Increasing concern for urban public safety has motivated the deployment of a large number of surveillance cameras in open spaces such as city squares, stations, and shopping malls. The efficient detection of crowd dynamics in urban open spaces using multi‐viewpoint surveillance videos continues to be a fundamental problem in the field of urban security. The use of existing methods for extracting features from video images has resulted in significant progress in single‐camera image space. However, surveillance videos are geotagged videos with location information, and few studies have fully exploited the spatial semantics of these videos. In this study, multi‐viewpoint videos in geographic space are used to fuse object trajectories for crowd sensing and spatiotemporal analysis. The YOLOv3‐DeepSORT model is used to detect a pedestrian and extract the corresponding image coordinates, combine spatial semantics (such as the positions of the pedestrian in the field of view of the camera) to build a projection transformation matrix and map the object recorded by a single camera to geographic space. Trajectories from multi‐viewpoint videos are fused based on the features of location, time, and directions to generate a complete pedestrian trajectory. Then, crowd spatial pattern analysis, density estimation, and motion trend analysis are performed. Experimental results demonstrate that the proposed method can be used to identify crowd dynamics and analyze the corresponding spatiotemporal pattern in an urban open space from a global perspective, providing a means of intelligent spatiotemporal analysis of geotagged videos.

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Section: Related Studiesmentioning

confidence: 99%

Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos

Liu

Han

Song

et al. 2023

Transactions in GIS

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“…We may calculate the homography matrix by using selected reference points between the picture and the world to establish the perspective correction model, which is used to determine the single-pixel actual size in the map. The suggested approach for perspective correction is applied to detect moving objects (Lin et al, 2020).…”

Section: Geometric Measurement Of the Target On A Single Imagementioning

confidence: 99%

“…The transformation process of the PTZ camera's image to world coordinate is nonlinear (Lin et al, 2020) and the pixel coordinates of the same target in different PTZ parameter images bring the challenge of an explosion of mapping related to the traditional single image to the solution of the homography matrix. Facing a large number of world-image pairs and homography matrices required by a single image in solving the homography transformation under multiple PTZ views, the traditional homography transformation is considered to be not suitable for the geolocation of the PTZ camera.…”

Section: Technology Roadmapmentioning

confidence: 99%

WLTOG: An optimization approach for wild large‐range target omnidirectional geolocation based on monocular PTZ camera

Zhou

Yuan

Tang

et al. 2022

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The pan‐tilt‐zoom (PTZ) cameras are increasingly popular in urban management and resource protection due to their omnidirectional coverage and reasonable cost. Traditionally, the research of monocular camera geolocation focuses on small‐range ones with a central projection camera under ideal conditions, making it challenging to meet the requirements of omnidirectional coverage and large‐range geolocation for PTZ cameras. In this article, a wild large‐range target omnidirectional geolocation (WLTOG) algorithm was proposed. Firstly, the WLTOG algorithm is used to transform the target in a single image to the camera's optical axis, thereby obtaining the camera posture value. After that, by using a high‐order overdetermined equation, it fits the nonlinear mapping relationship between multiple pairs of PTZ camera postures and the geospatial coordinates. At last, it realizes the bidirectional calculation between pixel coordinates and geospatial coordinates. The experimental PTZ camera is mounted on a communication tower in a wild environment. Therefore, this method can be utilized to construct an omnidirectional geolocation relationship that meets the requirements of omnidirectional geolocation within 945 m based on unknown accurate internal and external orientation elements. The WLTOG algorithm is conducive to the geometric measurement of video and GIS‐augmented video surveillance.

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“…The result shows that the moving object detection result, TP, of the MOD-AT algorithm is closer to TN. The box diagram in Figure 10b,d, shows the average variability of the four algorithms, GVIBE [39], IGMM [34], NPCM [38], and MOD-AT, compared with the number of TN detections. These indicators show that 1.…”

Section: Single-frame Accuracy Verificationmentioning

confidence: 99%

“…The area weight of the object in the image space is obtained to filter part of the moving object detection interference. Lin et al [38] established a single mapping relationship between video image space and geographic space and realized interference filtering from the external environment based on the non-linear-perspective correction model algorithm (NPCM). However, the above algorithm only considers the linear or non-linear characteristics of the object on the image and does not consider the projection distortion of the object size caused by the camera-imaging mechanism.…”

mentioning

confidence: 99%

Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

Luo

Wang

Cai

et al. 2021

IJGI

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Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.

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A method of perspective normalization for video images based on map data

Cited by 3 publications

References 9 publications

Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos

Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos

WLTOG: An optimization approach for wild large‐range target omnidirectional geolocation based on monocular PTZ camera

Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

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