Non-Overlapping Multi-camera Detection and Tracking of Vehicles in Tunnel Surveillance

Castañeda, Jorge Niño; Jelaca, Vedran; Frias, Andres; Pižurica, Aleksandra; Philips, Wilfried; Cabrera, Reyes Rios; Tuytelaars, Tinne

doi:10.1109/dicta.2011.105

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…Based survey outcome, it produced a novel wireless video sensor network at low-cost framework design. Jorge, 2011 [172] Computer vision and vehicle tracking.…”

Section: Computer Vision and Tracking Techniquementioning

confidence: 99%

Literature Survey on Multi-Camera System and Its Application

2020

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A multi-camera system combines features from different cameras to exploit a scene of an event to increase the output image quality. The combination of two or more cameras requires prior settings in terms of calibration and architecture. Therefore, this paper surveys the available literature in terms of multi-camera systems' physical arrangements, calibrations, algorithms, and their advantages and disadvantages. We also survey the recent developments and advancements in four areas of multi-camera system applications, which are surveillance, sports, education, and mobile phones. In the surveillance system, the combination of multiple heterogeneous cameras and the discovery of Pan-Tilt-Zoom (PTZ) and smart cameras have brought tremendous achievements in the area of multi-camera control and coordination. Different approaches have been proposed to facilitate effective collaboration and monitoring among the camera network. Furthermore, the application of multi-cameras in sports has made the games more interesting in the aspect of analyses and transparency. The application of the multi-camera system in education has taken education beyond the four walls of the class. The method of teaching, student attendance enrollment, determination of students' attention, teacher and student assessment can now be determined with ease, and all forms of proxy and manipulation in education can be reduced by using a multi-camera system. Besides, the number of cameras featuring on smartphones is gaining noticeable recognition. However, most of these cameras serve different purposes, from zooming, telephoto, and wider Field of View (FOV). Therefore, future smartphones should be expecting more cameras or the development would be in a different direction.

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“…Based survey outcome, it produced a novel wireless video sensor network at low-cost framework design. Jorge, 2011 [172] Computer vision and vehicle tracking.…”

Section: Computer Vision and Tracking Techniquementioning

confidence: 99%

Literature Survey on Multi-Camera System and Its Application

2020

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“…The first group, which consists of camera views with non‐overlapping regions, has been actively studied. For instance, in , the authors proposed a real‐time multi‐camera tracking method to follow vehicles in a tunnel with multiple non‐overlapping cameras. The Adaboost detector is used for vehicle detection, and the Kalman filter is used for object tracking.…”

Section: Related Workmentioning

confidence: 99%

Multi‐camera‐based security log management scheme for smart surveillance

Kim

Hwang

Rho

2013

Security Comm Networks

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In this paper, we propose a new security log management scheme for smart surveillance in a multi‐camera environment. Basically, our security log consist of descriptions for various behavior properties of moving objects, such as motion type, time, and speed in a merged camera view. To generate such security log, we first analyze the input video frame from each surveillance camera and construct a motion vector of interest points in the frame. By analyzing the motion vector, we recognize moving objects and trace their local behavior in the video. On the basis of this analysis, we can calculate various global behavior features of the objects in the merged camera view, which can be acquired by stitching together the frames from multiple camera inputs. Such global behavior features are captured into security logs, which can be used to smartly carry out various surveillance operations such as retrieving objects whose behavior is similar to a query behavior or whose behavior shows predefined abnormality. Because our scheme treats all the objects in the frame independently, it can handle multiple objects simultaneously. We implemented a prototype system and performed various experiments to demonstrate that our scheme can achieve a reasonable performance. Copyright © 2013 John Wiley & Sons, Ltd.

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“…In this method, vehicle matching is performed using CNN features to obtain continuous vehicle trajectories, but it does not contain 3D physical information of vehicles. Castaneda et al [ 19 ] proposed a multi-camera detection and vehicle tracking method in nonoverlapping tunnel scenes using optical flow and Kalman filters. This method can be combined with camera-to-camera vehicle travel time and lane position constraints to obtain continuous vehicle trajectory, which can solve vehicle occlusion problems to some extent.…”

Section: Introductionmentioning

confidence: 99%

3D Vehicle Trajectory Extraction Using DCNN in an Overlapping Multi-Camera Crossroad Scene

Heo

Kwon

2021

Sensors

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The 3D vehicle trajectory in complex traffic conditions such as crossroads and heavy traffic is practically very useful in autonomous driving. In order to accurately extract the 3D vehicle trajectory from a perspective camera in a crossroad where the vehicle has an angular range of 360 degrees, problems such as the narrow visual angle in single-camera scene, vehicle occlusion under conditions of low camera perspective, and lack of vehicle physical information must be solved. In this paper, we propose a method for estimating the 3D bounding boxes of vehicles and extracting trajectories using a deep convolutional neural network (DCNN) in an overlapping multi-camera crossroad scene. First, traffic data were collected using overlapping multi-cameras to obtain a wide range of trajectories around the crossroad. Then, 3D bounding boxes of vehicles were estimated and tracked in each single-camera scene through DCNN models (YOLOv4, multi-branch CNN) combined with camera calibration. Using the abovementioned information, the 3D vehicle trajectory could be extracted on the ground plane of the crossroad by calculating results obtained from the overlapping multi-camera with a homography matrix. Finally, in experiments, the errors of extracted trajectories were corrected through a simple linear interpolation and regression, and the accuracy of the proposed method was verified by calculating the difference with ground-truth data. Compared with other previously reported methods, our approach is shown to be more accurate and more practical.

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Non-Overlapping Multi-camera Detection and Tracking of Vehicles in Tunnel Surveillance

Cited by 14 publications

References 15 publications

Literature Survey on Multi-Camera System and Its Application

Literature Survey on Multi-Camera System and Its Application

Multi‐camera‐based security log management scheme for smart surveillance

3D Vehicle Trajectory Extraction Using DCNN in an Overlapping Multi-Camera Crossroad Scene

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