%0 Conference Paper %1 intriagopazmino2017algorithms %A Intriago-Pazmiño, Monserrate %A Vargas-Sandoval, Vanessa %A Moreno-Díaz, Jorge %A Salazar-Jácome, Elizabeth %A Salazar-Grande, Mayra %B Recent Advances in Information Systems and Technologies %C Cham %D 2017 %E Rocha, Álvaro %E Correia, Ana Maria %E Adeli, Hojjat %E Reis, Luís Paulo %E Costanzo, Sandra %I Springer %K computer-vision digital-image-processing human-recognition pedestrian-recognition people-recognition real systematic-review %P 436-446 %R 10.1007/978-3-319-56538-5_44 %T Algorithms for People Recognition in Digital Images: A Systematic Review and Testing %U https://doi.org/10.1007/978-3-319-56538-5_44 %X People recognition in digital images has wide applications and challenges. In this article, we present a systematic review of works published in the last decade; based on which, we have identified, implemented and tested the frequently used and best-assessed algorithms. We have found Histograms of Oriented Gradients (HOG) like feature extraction algorithm; and two classification algorithms, AdaBoost and Support Vector Machine (SVM). The tests were performed on 50 images chosen randomly from Penn-Fudan public database. The accuracy in SVM-HOG combination was 0.96, it is a similar value to a related work; and the detection rate was 0.66 in SVM-HOG combination and 0.72 in Adaboost-HOG combination, they are inferior to related works. We shall discuss possible reasons. %@ 978-3-319-56538-5

@inproceedings{intriagopazmino2017algorithms, abstract = {People recognition in digital images has wide applications and challenges. In this article, we present a systematic review of works published in the last decade; based on which, we have identified, implemented and tested the frequently used and best-assessed algorithms. We have found Histograms of Oriented Gradients (HOG) like feature extraction algorithm; and two classification algorithms, AdaBoost and Support Vector Machine (SVM). The tests were performed on 50 images chosen randomly from Penn-Fudan public database. The accuracy in SVM-HOG combination was 0.96, it is a similar value to a related work; and the detection rate was 0.66 in SVM-HOG combination and 0.72 in Adaboost-HOG combination, they are inferior to related works. We shall discuss possible reasons.}, added-at = {2019-11-14T18:11:40.000+0100}, address = {Cham}, author = {Intriago-Pazmiño, Monserrate and Vargas-Sandoval, Vanessa and Moreno-Díaz, Jorge and Salazar-Jácome, Elizabeth and Salazar-Grande, Mayra}, biburl = {https://www.bibsonomy.org/bibtex/2e8548e289a00276854cecfd755208f78/jpmor}, booktitle = {Recent Advances in Information Systems and Technologies}, description = {Algorithms for People Recognition in Digital Images: A Systematic Review and Testing | SpringerLink}, doi = {10.1007/978-3-319-56538-5_44}, editor = {Rocha, Álvaro and Correia, Ana Maria and Adeli, Hojjat and Reis, Luís Paulo and Costanzo, Sandra}, eventtitle = {World Conference on Information Systems and Technologies}, interhash = {42d38bb2bf0878030cbe91ac959701be}, intrahash = {e8548e289a00276854cecfd755208f78}, isbn = {978-3-319-56538-5}, keywords = {computer-vision digital-image-processing human-recognition pedestrian-recognition people-recognition real systematic-review}, language = {English}, pages = {436-446}, publisher = {Springer}, school = {Escuela Politécnica Nacional (EPN)}, timestamp = {2020-10-07T13:36:50.000+0200}, title = {Algorithms for People Recognition in Digital Images: A Systematic Review and Testing}, url = {https://doi.org/10.1007/978-3-319-56538-5_44}, year = 2017 }