ارائه ي روشي جديد براي شناسايي عابر پياده در تصاوير با استفاده از هيستوگرام گراديان جهت دار

چكيده انگليسي :

New Approach for Human Detection Based on Histograms of Oriented Gradients Narges Ghaedi Bardeh n ghaedibardeh@ec iut ac ir Date of Submission 2012 10 21 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Supervisor Maziar Palhang palhang@cc iut ac ir Abstract Designing a system which could detect humans in images is one of the most important subcatogories of computer vision Human detection has many usefull applications such as surveillance systems in buildings driver assistant systems robotics virtual reality automatic analysis of digital media content etc Due to the fact that humans are non rigid and articulated objects they could appear in images in many differnet shapes Occlusion variance in illumination appearance and scale are other difficulties that make the problem of human detection of very high complixity So far many various methods have been introduced to do the task of human detection but none of them could detect all of the input images completely Most human detection methods have two steps In the first one information of each image is representd by a descriptor in the second step a calssifying method is used to detect humans in input images Human detection is a two class problem humans and non humans so to learn the difference between these two classes training process is needed There are many descriptors available to describe the image information These descriptors could be devided into two subcategories global and local descriptors Global descriptors describe the image as a whole In other words global methods decode the whole image into a single vector In contrast local descriptors do not use the whole image information and try to find significant key points Histograms of Oriented Gradients HoG is an example of global descriptors which was introduced by Dalal in 2005 and has been widely used in human detection systems since The aim of this thesis is to propose a new method for human detection In order to represent the image information bag of features model is used Features are image patches extracted densely and then described by Histograms of Oriented Gradients descriptor To form our codebook of visual words the extracted patches are clustered using a clustering method like K means algorithm The center of each cluster is considered as a visual word Training images are described with these visual words so each picture would be represented by a vector which has the length of our visual words Finding the exact number of visual words is not an easy task Automatic methods could be used In this thesis we experimentally found the number of the clusters by applying the trained detector with differnet number of clusters on the test images To highlight the most important features a weigthing method could be applied to the descriptor vectors Here we used Term Frequency Inverse Document Frequency Tf Idf which has been used in data mining and text clustering In the proposed approach Support Vector Machine SVM is used as the binary classifier We applied our proposed method to the MIT and INRIA datasets and compared the performance of our algorithm with a similar method in the literature The results of our experiments show that our method performs at least as well as other available methods Keywords Computer vision Human Detection Histograms of Oriented Gradients HoG Bag of Features Tf Idf Weighting

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ارائه ي روشي جديد براي شناسايي عابر پياده در تصاوير با استفاده از هيستوگرام گراديان جهت دار

شناسايي انسان , مدل كيف ويژگي ها , وزن دهي tf-idf