TY - GEN
T1 - A-CCNN
T2 - 25th IEEE International Conference on Image Processing, ICIP 2018
AU - Amirgholipour, Saeed
AU - He, Xiangjian
AU - Jia, Wenjing
AU - Wang, Dadong
AU - Zeibots, Michelle
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community. Although many attempts have been reported, real-world problems, such as huge variation in subjects' sizes in images and serious occlusion among people, make it still a challenging problem. In this paper, we propose an Adaptive Counting Convolutional Neural Network (A-CCNN) and consider the scale variation of objects in a frame adaptively so as to improve the accuracy of counting. Our method takes advantages of contextual information to provide more accurate and adaptive density maps and crowd counting in a scene. Extensively experimental evaluation is conducted using different benchmark datasets for object-counting and shows that the proposed approach is effective and outperforms state-of-the-art approaches.
AB - Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community. Although many attempts have been reported, real-world problems, such as huge variation in subjects' sizes in images and serious occlusion among people, make it still a challenging problem. In this paper, we propose an Adaptive Counting Convolutional Neural Network (A-CCNN) and consider the scale variation of objects in a frame adaptively so as to improve the accuracy of counting. Our method takes advantages of contextual information to provide more accurate and adaptive density maps and crowd counting in a scene. Extensively experimental evaluation is conducted using different benchmark datasets for object-counting and shows that the proposed approach is effective and outperforms state-of-the-art approaches.
KW - Adaptive Counting CNN
KW - Crowd counting
KW - Scale Variation
UR - http://www.scopus.com/inward/record.url?scp=85062913713&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2018.8451399
DO - 10.1109/ICIP.2018.8451399
M3 - Conference contribution
AN - SCOPUS:85062913713
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 948
EP - 952
BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PB - IEEE Computer Society
Y2 - 7 October 2018 through 10 October 2018
ER -