H.3.2.2. Computer vision
Zobeir Raisi; Valimohammad Nazarzehi; Rasoul Damani; Esmaeil Sarani
Abstract
This paper explores the performance of various object detection techniques for autonomous vehicle perception by analyzing classical machine learning and recent deep learning models. We evaluate three classical methods, including PCA, HOG, and HOG alongside different versions of the SVM classifier, and ...
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This paper explores the performance of various object detection techniques for autonomous vehicle perception by analyzing classical machine learning and recent deep learning models. We evaluate three classical methods, including PCA, HOG, and HOG alongside different versions of the SVM classifier, and five deep-learning models, including Faster-RCNN, SSD, YOLOv3, YOLOv5, and YOLOv9 models using the benchmark INRIA dataset. The experimental results show that although classical methods such as HOG + Gaussian SVM outperform other classical approaches, they are outperformed by deep learning techniques. Furthermore, Classical methods have limitations in detecting partially occluded, distant objects and complex clothing challenges, while recent deep-learning models are more efficient and provide better performance (YOLOv9) on these challenges.
H.5. Image Processing and Computer Vision
Seyed M. Ghazali; Y. Baleghi
Abstract
Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric ...
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Observation in absolute darkness and daytime under every atmospheric situation is one of the advantages of thermal imaging systems. In spite of increasing trend of using these systems, there are still lots of difficulties in analysing thermal images due to the variable features of pedestrians and atmospheric situations. In this paper an efficient method is proposed for detecting pedestrians in outdoor thermal images that adapts to variable atmospheric situations. In the first step, the type of atmospheric situation is estimated based on the global features of the thermal image. Then, for each situation, a relevant algorithm is performed for pedestrian detection. To do this, thermal images are divided into three classes of atmospheric situations: a) fine such as sunny weather, b) bad such as rainy and hazy weather, c) hot such as hot summer days where pedestrians are darker than background. Then 2-Dimensional Double Density Dual Tree Discrete Wavelet Transform (2D DD DT DWT) in three levels is acquired from input images and the energy of low frequency coefficients in third level is calculated as the discriminating feature for atmospheric situation identification. Feed-forward neural network (FFNN) classifier is trained by this feature vector to determine the category of atmospheric situation. Finally, a predetermined algorithm that is relevant to the category of atmospheric situation is applied for pedestrian detection. The proposed method in pedestrian detection has high performance so that the accuracy of pedestrian detection in two popular databases is more than 99%.
