Feature-based tracking on a multi-omnidirectional camera dataset

Demiroz, Bans Evrim; Arı, İsmail; Eroglu, Orhan; Salah, Albert Ali; Akarun, Lale

doi:10.1109/isccsp.2012.6217867

Cited by 38 publications

(14 citation statements)

References 13 publications

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“…Somewhat different is the dataset proposed by Demiröz et al [18], where 360 • cameras are mounted on the ceiling of a room and a sidewall; thus, there is no advantage of using a 360 • camera to acquire the whole environment. Many datasets from fisheye cameras are available in the literature, especially for autonomous driving.…”

Section: • Videos Datasetsmentioning

confidence: 99%

A Dataset of Annotated Omnidirectional Videos for Distancing Applications

et al. 2021

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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.

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Section: • Videos Datasetsmentioning

confidence: 99%

A Dataset of Annotated Omnidirectional Videos for Distancing Applications

et al. 2021

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“…Another people detector on omnidirectional images is (Krams and Kiryati, 2017) that use DET curves on BOMNI database (Demiröz et al, 2012) for evaluation, therefore we don't compare our results to (a) Precision-recall curve on PIROPO (b) Precision-recall curve on our own data (Flat) Figure 3: The precision-recall curve for NMS, Soft-NMS and Soft-NMS with Gaussian smoothing function on two different omnidirectional image datasets from back-projected perspective views. The precision-recall curve of NMS Omni show the direct application of YOLOv2 to the omnidirectional images.…”

Section: Ground Truth Evaluationmentioning

confidence: 99%

Improved Person Detection on Omnidirectional Images with Non-maxima Supression

Seidel

Apitzsch

Hirtz

2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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We propose a person detector on omnidirectional images, an accurate method to generate minimal enclosing rectangles of persons. The basic idea is to adapt the qualitative detection performance of a convolutional neural network based method, namely YOLOv2 to fish-eye images. The design of our approach picks up the idea of a state-of-the-art object detector and highly overlapping areas of images with their regions of interests. This overlap reduces the number of false negatives. Based on the raw bounding boxes of the detector we fine-tuned overlapping bounding boxes by three approaches: non-maximum suppression, soft non-maximum suppression and soft non-maximum suppression with Gaussian smoothing. The evaluation was done on the PIROPO database and an own annotated Flat dataset, supplemented with bounding boxes on omnidirectional images. We achieve an average precision of 64.4 % with YOLOv2 for the class person on PIROPO and 77.6 % on Flat. For this purpose we fine-tuned the soft non-maximum suppression with Gaussian smoothing.

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“…Deneylerimizde BOMNI [7] veri kümesindeki birden fazla insanın etkileştigi videoları kullandık. Veri kümesindeki videolar bir odanın tavanına ve yan duvarına monte edilen iki adet heryönlü kamerayla elde edilmiştir.…”

Section: Deneyler Ve Sonuçlarunclassified

“…Belli bir konumda duran bir insan önplan maskesi üretmek için, 3 boyutta o konuma 16cm×16cm×180cm boyutlarında kare prizma konularak her kamera için görüntü düzlemine izdüşümü alınmıştır. Tablo I: Önerilen yöntemle BOMNI veri kümesi üzerinde elde edilmiş birden fazla insan takibi sonuçları ve önceki [7] çalışması ile kıyaslaması.…”

Section: Deneyler Ve Sonuçlarunclassified

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Multiple person tracking using omnidirectional cameras

Demiröz

Salah

Akarun

2014

2014 22nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe -Video görüntülerinden insan takibi, güvenlik uygulamaları başta olmak üzere bir çok alanda kritik öneme sahiptir. Bu çalışmada, heryönlü kameralardan elde edilen görüntüler kullanarak insanların ayrık konumlarda bulunma olasılıklarını degişimsel Bayesçi yöntemlerle hesaplayan ve takip problemini bu olasılıklardan faydalanarak bir akım eniyileme problemi olarak ifade edip çözen bir yöntem sunmaktayız. BOMNI veri kümesi üzerinde yaptıgımız deneylerde önceki çalışmalarda %68.18 olarak rapor edilmiş takip performansını (MOTA) %86.39'a çıkarttık.Abstract-Person tracking in videos is crucial in different areas such as security applications. In this work we present a method that first finds human presence probabilities on discrete locations via variational Bayesian inference using images obtained from omnidirectional cameras and then uses that information to solve the tracking problem as a flow optimization problem. In our experiments on the BOMNI dataset, we have increased tracking performance (MOTA) to %86.39, which was reported as %68.18 using the baseline method.

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Feature-based tracking on a multi-omnidirectional camera dataset

Cited by 38 publications

References 13 publications

A Dataset of Annotated Omnidirectional Videos for Distancing Applications

A Dataset of Annotated Omnidirectional Videos for Distancing Applications

Improved Person Detection on Omnidirectional Images with Non-maxima Supression

Multiple person tracking using omnidirectional cameras

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