{"id":201,"date":"2015-10-23T07:36:49","date_gmt":"2015-10-23T07:36:49","guid":{"rendered":"http:\/\/www.cs.ubbcluj.ro\/~meco\/?p=201"},"modified":"2026-02-01T12:09:34","modified_gmt":"2026-02-01T12:09:34","slug":"pedestrian-recognition-using-a-dynamic-modality-fusion-approach","status":"publish","type":"post","link":"https:\/\/www.cs.ubbcluj.ro\/~meco\/pedestrian-recognition-using-a-dynamic-modality-fusion-approach\/","title":{"rendered":"Pedestrian recognition using a dynamic modality fusion approach (2015)"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\">Abstract<\/h3>\n\n\n\n<p>It was proved that the fusion of information from multi-modality images increases the accuracy of pedestrian recognition systems. One of the best approach so far is to concatenate the features from multi-modality images into a large feature vector, but it requires strong camera calibration settings and non-discriminative modalities could lead to missclassification of some particular images. We present a modality fusion approach for pedestrian recognition, which is able to dynamically select and fuse the most discriminative modalities for a given image and furthermore use them in the classification process. Firstly, we extract kernel descriptor features from a given image in three modalities: intensity, depth and flow. Secondly, we dynamically determine the most suitable modalities for that image using a modality pertinence classifier. Thirdly, we join the features from the selected modalities and classify the image using a linear SVM approach. Numerical experiments are performed on the Daimler benchmark dataset consisting of pedestrian and non-pedestrian bounding boxes captured in outdoor urban environments and indicate that our model outperforms all the individual-modality classifiers and is slightly better than the model obtained by concatenating all multi-modality features.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Citare<\/h3>\n\n\n\n<p>Rus, A., Rogozan, A., Dio\u0219an, L., Benshrair, A., Pedestrian recognition using a dynamic modality fusion approach, ICCP, 2015, 393-400&nbsp;<br><a href=\"https:\/\/doi.org\/10.1109\/ICCP.2015.7312691\">https:\/\/doi.org\/10.1109\/ICCP.2015.7312691<\/a><br><\/p>\n","protected":false},"excerpt":{"rendered":"<p>It was proved that the fusion of information from multi-modality images increases the accuracy of pedestrian recognition systems. One of the best approach so far is to concatenate the features from multi-modality images into a large feature vector, but it requires strong camera calibration settings and non-discriminative modalities could lead to missclassification of some particular images. We present a modality fusion approach for pedestrian recognition, which is able to dynamically select and fuse the most discriminative modalities for a given image and furthermore use them in the classification process. Firstly, we extract kernel descriptor features from a given image in three modalities: intensity, depth and flow. Secondly, we dynamically determine the most suitable modalities for that image using a modality pertinence classifier. Thirdly, we join the features from the selected modalities and classify the image using a linear SVM approach. Numerical experiments are performed on the Daimler benchmark dataset consisting of pedestrian and non-pedestrian bounding boxes captured in outdoor urban environments and indicate that our model outperforms all the individual-modality classifiers and is slightly better than the model obtained by concatenating all multi-modality features.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[4],"tags":[11],"_links":{"self":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/201"}],"collection":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/comments?post=201"}],"version-history":[{"count":3,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/201\/revisions"}],"predecessor-version":[{"id":1575,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/201\/revisions\/1575"}],"wp:attachment":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/media?parent=201"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/categories?post=201"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/tags?post=201"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}