TY - GEN
T1 - A comparative study and state-of-the-art evaluation for pedestrian detection
AU - Baabou, Salwa
AU - Abubakr, Abdelrahman G.
AU - Bremond, Francois
AU - Fradj, Awatef Ben
AU - Ben Farah, Mohamed Lamine
AU - Kachouri, Abdennaceur
PY - 2019/5/16
Y1 - 2019/5/16
N2 - Pedestrian detection has many applications in computer vision including robotics, scene understanding, person reidentification and video-surveillance system. In fact, the process of person detection aims to detect and localize each person in the images, represented via bounding boxes. Recent deep learning pedestrian detectors, which are hybrid methods that combines traditional hand-crafted features and deep convolutional features such as Fast/Faster Region based-CNN (R-CNN), have shown excellent performance for general object detection. In this context, we propose in this paper an overview of the state-of-the-art performance of current deep learning pedestrian detectors and a comparison of these detectors is provided. Evaluation criteria, popular datasets used for evaluation and a quantitative results are also described and discussed in this work.
AB - Pedestrian detection has many applications in computer vision including robotics, scene understanding, person reidentification and video-surveillance system. In fact, the process of person detection aims to detect and localize each person in the images, represented via bounding boxes. Recent deep learning pedestrian detectors, which are hybrid methods that combines traditional hand-crafted features and deep convolutional features such as Fast/Faster Region based-CNN (R-CNN), have shown excellent performance for general object detection. In this context, we propose in this paper an overview of the state-of-the-art performance of current deep learning pedestrian detectors and a comparison of these detectors is provided. Evaluation criteria, popular datasets used for evaluation and a quantitative results are also described and discussed in this work.
KW - convolutional neural network (CNN)
KW - deep learning
KW - pedestrian detection
U2 - 10.1109/STA.2019.8717226
DO - 10.1109/STA.2019.8717226
M3 - Conference contribution book
AN - SCOPUS:85067133627
T3 - 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2019
SP - 485
EP - 490
BT - 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2019
PB - IEEE
CY - Piscataway, NJ.
T2 - 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2019
Y2 - 24 March 2019 through 26 March 2019
ER -