FDDB-360: Face Detection in 360-Degree Fisheye Images

Fu, Jianglin; Alvar, Saeed Ranjbar; Bajić, Ivan V.; Vaughan, Rodney G.

doi:10.1109/mipr.2019.00011

Cited by 15 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The corresponding annotations were also converted into the fisheye image coordinate system. Square patches were sampled from the original images and converted to fisheye-looking images using the following generic transformation [3]:true(x',y'true)=true(x⋅1−y22,y⋅1−x22true)…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Simon Fraser University, School of Engineering Science. Latitude: 49.276765, Longitude: 122.917957Data accessibilityPublic.VOC-360: https://doi.org/10.25314/ca0092b1-1e87-4928-b5f5-ebae30decb8dWider-360: https://researchdata.sfu.ca/pydio_public/c09804Related research articleThis is a direct submission to Data in Brief, the most relevant research article from the reference list is [3]. …”

mentioning

confidence: 99%

See 1 more Smart Citation

Datasets for face and object detection in fisheye images

Bajić

Vaughan

2019

Data in Brief

Self Cite

View full text Add to dashboard Cite

We present two new fisheye image datasets for training object and face detection models: VOC-360 and Wider-360. The fisheye images are created by post-processing regular images collected from two well-known datasets, VOC2012 and Wider Face, using a model for mapping regular to fisheye images implemented in Matlab. VOC-360 contains 39,575 fisheye images for object detection, segmentation, and classification. Wider-360 contains 63,897 fisheye images for face detection. These datasets will be useful for developing face and object detectors as well as segmentation modules for fisheye images while the efforts to collect and manually annotate true fisheye images are underway.

show abstract

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Datasets for face and object detection in fisheye images

Bajić

Vaughan

2019

Data in Brief

Self Cite

View full text Add to dashboard Cite

show abstract

“…With respect to traditional techniques [20][21][22], the equirectangular projection introduces position-dependent distortions which must be considered when devising specific algorithms. This is a very new open issue, but some works have already been published [23,24]. To the best of our knowledge, there are no available datasets of videos acquired by an omnidirectional camera network (that is one of our future works), which are necessary in object re-identification applications [25].…”

Section: • Videos Datasetsmentioning

confidence: 99%

A Dataset of Annotated Omnidirectional Videos for Distancing Applications

et al. 2021

View full text Add to dashboard Cite

Omnidirectional (or 360°) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360° videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360° image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications.

show abstract

“…9(a)), which means that (0, 0) represents the center and (±1, ±1) are the coordinates of four corners, respectively. Then we introduce the radial distortion [57], in which straight lines bend outward from the center of the image. Specifically, the coordinate mapping between the pixels (x, y) on an original image and corresponding pixels (x , y ) in a fisheye image can be defined as:…”

Section: ) Fisheye-like Distortionmentioning

confidence: 99%

FisheyeDet: A Self-Study and Contour-Based Object Detector in Fisheye Images

Tong

Tang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Fisheye Images have attracted increasing attention from the research community due to their large field of view (LFOV). However, the geometric transformations inherent in fisheye cameras result in unknown spatial distortion and large variations in the appearance of objects. And this fact leads to poor performance of the state-of-the-art methods in conventional two-dimensional (2D) images. To address this problem, we propose a self-study and contour-based object detector in fisheye images, named FisheyeDet. The No-prior Fisheye Representation Method is proposed to guarantee that the network adaptively extracts distortion features without prior information such as prespecified lens parameters, special calibration patterns, etc. Furthermore, in order to tightly and robustly localize objects in fisheye images, the Distortion Shape Matching strategy is proposed, which invokes the irregular quadrilateral bounding boxes based on the contour of distorted objects as the core. By combining with the ''No-prior Fisheye Representation Method'' and ''Distortion Shape Matching'', our proposed detector builds an end-to-end network. Finally, due to the lack of public fisheye datasets, we are on the first attempt to create a multi-class fisheye dataset VOC-Fisheye for object detection. Our proposed detector shows favorable generalization ability and achieves 74.87% mAP (mean average precision) on the VOC-Fisheye, outperforming the existing state-of-the-art methods.INDEX TERMS Fisheye, object detection and recognition, large field of view (LFOV), deep learning.

show abstract

FDDB-360: Face Detection in 360-Degree Fisheye Images

Cited by 15 publications

References 14 publications

Datasets for face and object detection in fisheye images

Datasets for face and object detection in fisheye images

A Dataset of Annotated Omnidirectional Videos for Distancing Applications

FisheyeDet: A Self-Study and Contour-Based Object Detector in Fisheye Images

Contact Info

Product

Resources

About