Classification and tracking of traffic scene objects with hybrid camera systems

Baris, Ipek; Baştanlar, Yalın

doi:10.1109/itsc.2017.8317588

Cited by 8 publications

(7 citation statements)

References 26 publications

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“…Several authors have considered the fisheye cameras for ego vehicles. Among them, some notable works are classification and tracking of cars and pedestrians using hybrid cameras [32], pedestrian detection using a combination of synthetic and real images from a 360 • horizontal FOV camera [33]. In addition, WoodScape [34] is a multi-camera fisheye dataset, providing 360 • sensing around a vehicle with four fisheye lenses.…”

Section: Real Datasetmentioning

confidence: 99%

Expandable Spherical Projection and Feature Concatenation Methods for Real-Time Road Object Detection Using Fisheye Image

Kim

Park

2022

Applied Sciences

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Fisheye lens cameras are widely used in such applications where a large field of view (FOV) is necessary. A large FOV can provide an enhanced understanding of the surrounding environment and can be an effective solution for detecting the objects in automotive applications. However, this comes with the cost of strong radial distortions and irregular size of objects depending on the location in an image. Therefore, we propose a new fisheye image warping method called Expandable Spherical Projection to expand the center and boundary regions in which smaller objects are mostly located. The proposed method produces undistorted objects especially in the image boundary and a less unwanted background in the bounding boxes. Additionally, we propose three multi-scale feature concatenation methods and provide the analysis of the influence from the three concatenation methods in a real-time object detector. Multiple fisheye image datasets are employed to demonstrate the effectiveness of the proposed projection and feature concatenation methods. From the experimental results, we find that the proposed Expandable Spherical projection and the LCat feature concatenation yield the best AP performance, which is up to 4.7% improvement compared to the original fisheye image datasets and the baseline model.

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Section: Real Datasetmentioning

confidence: 99%

Expandable Spherical Projection and Feature Concatenation Methods for Real-Time Road Object Detection Using Fisheye Image

Kim

Park

2022

Applied Sciences

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“…Baris et al [43] constructed a dual-camera system for target tracking and observation. The dual-camera system includes an omnidirectional camera and a PTZ camera.…”

Section: Device-level (Ptz Camera) Target Trackingmentioning

confidence: 99%

Cell-Based Target Localization and Tracking with an Active Camera

You

Xiao

et al. 2022

Applied Sciences

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This paper proposes a new method of target localization and tracking. The method consists of four parts. The first part is to divide the scene into multiple cells based on the camera’s parameters and calibrate the position and error of each vertex. The second part mainly uses the bounding box detection algorithm, YOLOv4, based on deep learning to detect and recognize the scene image sequence and obtain the type, length, width, and position of the target to be tracked. The third part is to match each vertex of the cell in the image and the cell in the scene, generate a homography matrix, and then use the PnP model to calculate the precise world coordinates of the target in the image. In this process, a cell-based accuracy positioning method is proposed for the first time. The fourth part uses the proposed PTH model to convert the obtained world coordinates into P, T, and H values for the purpose of actively tracking and observing the target in the scene with a PTZ camera. The proposed method achieved precise target positioning and tracking in a 50 cm ∗ 250 cm horizontal channel and a vertical channel. The experimental results show that the method can accurately identify the target to be tracked in the scene, can actively track the moving target in the observation scene, and can obtain a clear image and accurate trajectory of the target. It is verified that the maximum positioning error of the proposed cell-based positioning method is 2.31 cm, and the average positioning error is 1.245 cm. The maximum error of the proposed tracking method based on the PTZ camera is 1.78 degrees, and the average error is 0.656 degrees.

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“…Research in these areas has also led to the release of several task-specific omnidirectional data sets focusing on structure from motion [19], [20], visual odometry [7], [21], human detection [22] and tracking [23], [24], and vehicle detection [25] and recognition [26], [27]. We note that the ground truth image annotations provided with each of these data sets are reliant on human input, with the exception of AMUSE [7].…”

Section: Related Workmentioning

confidence: 99%

Automatic Ground Truths: Projected Image Annotations for Omnidirectional Vision

Stamatescu

Barsznica

Kim

et al. 2017

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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We present a novel data set made up of omnidirectional video of multiple objects whose centroid positions are annotated automatically. Omnidirectional vision is an active field of research focused on the use of spherical imagery in video analysis and scene understanding, involving tasks such as object detection, tracking and recognition. Our goal is to provide a large and consistently annotated video data set that can be used to train and evaluate new algorithms for these tasks. Here we describe the experimental setup and software environment used to capture and map the 3D ground truth positions of multiple objects into the image. Furthermore, we estimate the expected systematic error on the mapped positions. In addition to final data products, we release publicly the software tools and raw data necessary to re-calibrate the camera and/or redo this mapping. The software also provides a simple framework for comparing the results of standard image annotation tools or visual tracking systems against our mapped ground truth annotations.

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Classification and tracking of traffic scene objects with hybrid camera systems

Cited by 8 publications

References 26 publications

Expandable Spherical Projection and Feature Concatenation Methods for Real-Time Road Object Detection Using Fisheye Image

Expandable Spherical Projection and Feature Concatenation Methods for Real-Time Road Object Detection Using Fisheye Image

Cell-Based Target Localization and Tracking with an Active Camera

Automatic Ground Truths: Projected Image Annotations for Omnidirectional Vision

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