Integration of Multi-Camera Video Moving Objects and GIS

Xie, Yujia; Wang, Meizhen; Liu, Xuejun; Mao, Bo; Wang, Fei‐Yue

doi:10.3390/ijgi8120561

Cited by 15 publications

(14 citation statements)

References 40 publications

(52 reference statements)

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“…For the convenience of threshold calculation, it is necessary to establish the mapping relationship between the actual orientation of the object in geographic space and the geometric-imaging characteristics in image space. Compared with the traditional mapping method based on the camera model, the homography method is simpler and does not require camera-internal and external parameters [39]. Therefore, we use the homography method to construct the mapping relationship between image space and geographical space.…”

Section: Mapping Relation Calculation Based On Homography Methodsmentioning

confidence: 99%

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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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Section: Mapping Relation Calculation Based On Homography Methodsmentioning

confidence: 99%

“…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%

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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“…where t x and t y in Equation ( 2) are the translation vector parameters. k 1 , k 2 , k 3 , and k 4 are the affine transformation parameters [46]. Four or more pairs of feature points must be known to solve for H.…”

Section: Video Stabilizationmentioning

confidence: 99%

Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

Wang

Liu

et al. 2021

Remote Sensing

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Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the “Stretching-Bending” Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that the same level of performance was obtained for the recognition of work cycles with the UAV-SBSP as compared with the FC-SBSP, demonstrating the good applicability of this method. Therefore, the results of this study show that UAV remote sensing enables effective monitoring of earthmoving excavator work cycles in construction sites where monitoring cameras are not available for installation, and it can be used as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring.

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“…In the event of an emergency, traditional transit systems and rail transit systems are managed by the government to ensure the reliability of the systems. Finally, geographical information systems and wireless communication systems of public transit systems make it possible for timely transit-based evacuation when urban rail transit line emergencies occur [32]. The sets, parameters, and decision variables are defined in Table 1 before we present the mathematical formulation.…”

Section: Problem Description and Assumptionsmentioning

confidence: 99%

Transit-Based Evacuation for Urban Rail Transit Line Emergency

Hou

Cao

Lv³

et al. 2020

Sustainability

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Urban rail systems are the backbone of urban transit networks and are characterized by large passenger volumes, high speeds, punctuality, and low environmental impacts. However, unforeseen events such as rail transit line emergencies can lead to unexpected costs and delays. As a means of disruption management, we divide the decision support system for urban rail transit line emergency situations into two stages—transit-based evacuation and bus bridging management. This paper focuses on the transit-based evacuation under emergency scenarios on a single rail line. The model determines the vehicles and routes within traditional transit systems required to evacuate stranded passengers within a given time window. In addition, the proposed method ensures the reliability of traditional transit systems by considering the operating fleet and reserve fleet in the traditional transit systems. Therefore, the proposed optimization model is established with the objective of maximizing the total number of stranded passengers transferred within the given time window and headway constraint. Herein, we present the optimization model and solution method, and the proposed method is validated. The effectiveness of the proposed control method is evaluated in the Changchun urban transit network. By analyzing stranded passengers at stations under different numbers of vehicles and time periods, the results show that the proposed model can significantly provide routing arrangements to maximize the number of passengers evacuated from stations. The results are useful in the development of emergency evacuation plans to prevent secondary accidents and evacuate stranded passengers during a rail transit line emergency.

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Integration of Multi-Camera Video Moving Objects and GIS

Cited by 15 publications

References 40 publications

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

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

Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

Transit-Based Evacuation for Urban Rail Transit Line Emergency

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