Datasets for face and object detection in fisheye images

Fu, Jianglin; Bajić, Ivan V.; Vaughan, Rodney G.

doi:10.1016/j.dib.2019.104752

Cited by 19 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our implementation, the person detector is a Yolo-V3 detector [187] which detects the head and person bounding box simultaneously. The detector is trained on images from the Google OpenImage dataset [123] and a fisheye image dataset [73]. We use the detected head bounding boxes for people tracking.…”

Section: Short-term People Trackingmentioning

confidence: 99%

Ego4D: Around the World in 3,000 Hours of Egocentric Video

Grauman

Westbury²,

Byrne³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

300

188

View full text Add to dashboard Cite

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception.

show abstract

Section: Short-term People Trackingmentioning

confidence: 99%

Ego4D: Around the World in 3,000 Hours of Egocentric Video

Grauman

Westbury²,

Byrne³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

300

188

View full text Add to dashboard Cite

show abstract

“…The VOC-360 dataset is a publicly fisheye image dataset 10 , which present a spherical view. There are twenty categories in the VOC-360 dataset, including person, car, bicycle, dog, bird, and so on.…”

Section: Datamentioning

confidence: 99%

An improved YOLOv8s model based on sphere convolution for object detection in fisheye images

Zhou

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

View full text Add to dashboard Cite

At present, with the continuous development of computer vision technology, people's requirements for images are no longer limited to the degree of visual clarity, but pay more attention to the comprehensiveness of image information. Compared with the limited field of view of the traditional perspective camera, the fisheye camera has a blind-free field of view and can obtain rich visual information. However, the wide field of view of the fisheye camera causes large-scale variation and distortion problems, which creates a great challenge to the feature extraction of traditional object detection algorithms. According to the characteristics of fisheye images, we adopt the current state-of-the-art lightweight YOLOv8s model as the base model and introduce Sphere Convolution (SphereConv) to deal with the complex distortion characteristics of fisheye images. The proposed Sphere-YOLOv8s model effectively improves the distortion problem of fisheye images. The experimental results show that the Sphere-YOLOv8s model on the VOC-360 dataset obtains an excellent improvement in object detection compared to the original YOLOv8s model. Among them, Precision improves 12.5%, Recall improves 14.5%, mAP@0.5 improves 16.7%. mAP@0.5:0.95 improves 20.5%.

show abstract

“…In a similar way, two well-known datasets, VOC2012 [25] and Wider Face [26] are synthesized to fisheye images in [27]. For the stereo algorithm, 45k synthetic fisheye images in four orthogonal directions are presented in [28].…”

Section: Fisheye Dataset For Urban Driving 221 Synthetic Datasetmentioning

confidence: 99%

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

Kim

Park

2022

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Datasets for face and object detection in fisheye images

Cited by 19 publications

References 5 publications

Ego4D: Around the World in 3,000 Hours of Egocentric Video

Ego4D: Around the World in 3,000 Hours of Egocentric Video

An improved YOLOv8s model based on sphere convolution for object detection in fisheye images

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

Contact Info

Product

Resources

About