Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object Zamanda ortalaması alınmış ikili önplan imgeleri kullanarak taşıt sınıflandırması(IEEE, 2015) Karaimer, Hakkı Can; Baştanlar, YalınWe describe a shape-based method for classification of vehicles from omnidirectional videos. Different from similar approaches, the binary images of vehicles obtained by background subtraction in a sequence of frames are averaged over time. We show with experiments that using the average shape of the object results in a more accurate classification than using a single frame. The vehicle types we classify are motorcycle, car and van. We created an omnidirectional video dataset and repeated experiments with shuffled train-test sets to ensure randomization.Article Citation - WoS: 3Citation - Scopus: 4Affordable person detection in omnidirectional cameras using radial integral channel features(Springer Verlag, 2019) Demiröz, Barış Evrim; Salah, Albert Ali; Baştanlar, Yalın; Akarun, LaleOmnidirectional cameras cover more ground than perspective cameras, at the expense of resolution. Their comprehensive field of view makes omnidirectional cameras appealing for security and ambient intelligence applications. Person detection is usually a core part of such applications. Conventional methods fail for omnidirectional images due to different image geometry and formation. In this study, we propose a method for person detection in omnidirectional images, which is based on the integral channel features approach. Features are extracted from various channels, such as LUV and gradient magnitude, and classified using boosted decision trees. Features are pixel sums inside annular sectors (doughnut slice shapes) contained by the detection window. We also propose a novel data structure called radial integral image that allows to calculate sums inside annular sectors efficiently. We have shown with experiments that our method outperforms the previous state of the art and uses significantly less computational resources.Article Citation - WoS: 3Citation - Scopus: 3Elimination of Useless Images From Raw Camera-Trap Data(Türkiye Klinikleri Journal of Medical Sciences, 2019) Tekeli, Ulaş; Baştanlar, YalınCamera-traps are motion triggered cameras that are used to observe animals in nature. The number of images collected from camera-traps has increased significantly with the widening use of camera-traps thanks to advances in digital technology. A great workload is required for wild-life researchers to group and label these images. We propose a system to decrease the amount of time spent by the researchers by eliminating useless images from raw camera-trap data. These images are too bright, too dark, blurred, or they contain no animals To eliminate bright, dark, and blurred images we employ techniques based on image histograms and fast Fourier transform. To eliminate the images without animals, we propose a system combining convolutional neural networks and background subtraction. We experimentally show that the proposed approach keeps 99% of photos with animals while eliminating more than 50% of photos without animals. We also present a software prototype that employs developed algorithms to eliminate useless images.Conference Object Citation - WoS: 2Citation - Scopus: 8İnsansız Araçlar için Anlamsal Bölütleme ile İmge Tabanlı Konumlandırma(Institute of Electrical and Electronics Engineers Inc., 2019) Çınaroğlu, İbrahim; Baştanlar, YalınBilgisayarlı Görü alanındaki popülerliğini koruyan araştırma konularından birisi insansız araçlarda yer tespiti ve konumlandırmadır. Araçların konumlandırılmasında kullanılan GPS sistemlerinin bazı durumlarda faal olamadığı bilinen bir gerçektir ve bu yetersizlik imge tabanlı konumlandırma çalışmalarına hız vermiştir. Bizim çalışmamızda, araç içinden elde edilmiş Malaga şehir merkezi görüntülerinden oluşan bir veri tabanı kullanılarak imge tabanlı konumlandırma yapılmıştır. İlk olarak, anlamsal (semantik) bölütleme sonucunda elde edilen bir anlamsal betimleyici oluşturulmuş ve yaklaşık en yakın komşuluk araması tekniği de kullanılarak bir konumlandırma yapılmıştır. Ardından bu yöntemin başarısı, literatürde sıkça kullanılan yerel betimleyici tabanlı yöntemin başarısıyla kıyaslanmıştır. Ayrıca, bu iki yöntemin birleştirilmesi ile elde edilen melez bir yöntem önerilmiştir. Önerilen melez imge-tabanlı konumlandırmanın, sadece yerel betimleyici ve sadece anlamsal betimleyici kullanan yöntemden daha başarılı olduğu, dolayısıyla yerel betimleyici tabanlı yöntemlerin anlamsal betimleyiciler ile desteklenmesinin başarıyı artırdığı, deneysel sonuçlarla gösterilmiştir.Article Citation - WoS: 9Citation - Scopus: 13Training Cnns With Image Patches for Object Localisation(Institution of Engineering and Technology, 2018) Orhan, Semih; Baştanlar, YalınRecently, convolutional neural networks (CNNs) have shown great performance in different problems of computer vision including object detection and localisation. A novel training approach is proposed for CNNs to localise some animal species whose bodies have distinctive patterns such as leopards and zebras. To learn characteristic patterns, small patches which are taken from different body parts of animals are used to train models. To find object location, in a test image, all locations are visited in a sliding window fashion. Crops are fed into trained CNN and their classification scores are combined into a heat map. Later on, heat maps are converted to bounding box estimates for varying confidence scores. The localisation performance of the patch-based training approach is compared with Faster R-CNN – a state-of-the-art CNN-based object detection and localisation method. Experimental results reveal that the patch-based training outperforms Faster R-CNN, especially for classes with distinctive patterns.Conference Object Citation - WoS: 4Citation - Scopus: 13Classification and Tracking of Traffic Scene Objects With Hybrid Camera Systems(Institute of Electrical and Electronics Engineers Inc., 2018) Barış, İpek; Baştanlar, YalınIn a hybrid camera system combining an omnidirectional and a Pan-Tilt-Zoom (PTZ) camera, the omnidirectional camera provides 360 degree horizontal field-of-view, whereas the PTZ camera provides high resolution at a certain direction. This results in a wide field-of-view and high resolution camera system. In this paper, we exploit this hybrid system for real-time object classification and tracking for traffic scenes. The omnidirectional camera detects the moving objects and performs an initial classification using shape-based features. Concurrently, the PTZ camera classifies the objects using high resolution frames and Histogram of Oriented Gradients (HOG) features. PTZ camera also performs high-resolution tracking for the objects classified as the target class by the omnidirectional camera. The object types we worked on are pedestrian, motorcycle, car and van. Extensive experiments were conducted to compare the classification accuracy of the hybrid system with single camera alternatives.Article Citation - WoS: 16Citation - Scopus: 16Detection and Classification of Vehicles From Omnidirectional Videos Using Multiple Silhouettes(Springer Verlag, 2017) Karaimer, Hakkı Can; Barış, İpek; Baştanlar, YalınTo detect and classify vehicles in omnidirectional videos, we propose an approach based on the shape (silhouette) of the moving object obtained by background subtraction. Different from other shape-based classification techniques, we exploit the information available in multiple frames of the video. We investigated two different approaches for this purpose. One is combining silhouettes extracted from a sequence of frames to create an average silhouette, the other is making individual decisions for all frames and use consensus of these decisions. Using multiple frames eliminates most of the wrong decisions which are caused by a poorly extracted silhouette from a single video frame. The vehicle types we classify are motorcycle, car (sedan) and van (minibus). The features extracted from the silhouettes are convexity, elongation, rectangularity and Hu moments. We applied two separate methods of classification. First one is a flowchart-based method that we developed and the second is K-nearest neighbour classification. 60% of the samples in the dataset are used for training. To ensure randomization in the experiments, threefold cross-validation is applied. The results indicate that using multiple silhouettes increases the classification performance.Conference Object Tümyönlü ve Ptz Kameralar ile Taşıt Sınıflandırması(Institute of Electrical and Electronics Engineers Inc., 2016) Barış, İpek; Baştanlar, YalınÇalışmamızda trafik sahneleri üzerindeki araçların tespit edilip sınıflandırması için bir tümyönlü bir de PTZ (pantilt-zoom) kamera kullanan bir yöntem önerilmiştir. Önerilen yöntem, tümyönlü kamerada arkaplan çıkarımı sonrası saptanan nesnenin konumuna göre PTZ kamerayı uygun açıya yönlendirmekte ve PTZ kamerada yapılan ikincil tespit sonrası çıkarılan öznitelikler ile araç sınıflandırılmaktadır. Sınıflandırma başarısı ayrıca sadece tümyönlü kamerada yapılan sınıflandırma ile karşılaştırılmıştır. Üzerine çalışılan nesne tipleri motorsiklet, araba, dolmuş ve yayadır.Article Citation - WoS: 28Citation - Scopus: 28A Direct Approach for Object Detection With Catadioptric Omnidirectional Cameras(Springer Verlag, 2016) Çınaroğlu, İbrahim; Baştanlar, YalınIn this paper, we present an omnidirectional vision-based method for object detection. We first adopt the conventional camera approach that uses sliding windows and histogram of oriented gradients (HOG) features. Then, we describe how the feature extraction step of the conventional approach should be modified for a theoretically correct and effective use in omnidirectional cameras. Main steps are modification of gradient magnitudes using Riemannian metric and conversion of gradient orientations to form an omnidirectional sliding window. In this way, we perform object detection directly on the omnidirectional images without converting them to panoramic or perspective images. Our experiments, with synthetic and real images, compare the proposed approach with regular (unmodified) HOG computation on both omnidirectional and panoramic images. Results show that the proposed approach should be preferred.Article Citation - WoS: 11Citation - Scopus: 15A Simplified Two-View Geometry Based External Calibration Method for Omnidirectional and Ptz Camera Pairs(Elsevier Ltd., 2016) Baştanlar, YalınThe external calibration of a camera system is essential for most of the applications that involve an omnidirectional and a pan-tilt-zoom (PTZ) camera. The methods in the literature fall into two major categories; (1) a complete external calibration of the system which allows all degrees of freedom but highly time consuming, (2) spatial mapping between the pixel coordinates in omnidirectional camera and pan/tilt angles of the PTZ camera instead of explicitly computing the rotation and translation. Most methods in this category make restrictive assumptions about the camera setup such as optical axes of the cameras coincide. We propose an external calibration method that is effective and practical. Using the two-view geometry principles and making reasonable assumptions about the camera setup, calibration is performed with just two scene points. We extract rotation using the point correspondences in images. Locating the PTZ camera in the omnidirectional image is used to find the translation parameters and the real distance between the two scene points lets us compute the translation in correct scale. Results of the simulated and real image experiments show that our method works effectively in real world cases and its accuracy is comparable to the state-of-the-art methods.