Crowd counting is very important in many tasks such as video surveillance, traffic monitoring, public security, and urban planning, so it is a very important part of the intelligent transportation system. However, achieving an accurate crowd counting and generating a precise density map are still challenging tasks due to the occlusion, perspective distortion, complex backgrounds, and varying scales. In addition, most of the existing methods focus only on the accuracy of crowd counting without considering the correctness of a density distribution; namely, there are many false negatives and false positives in a generated density map. To address this issue, we propose a novel encoder-decoder Convolution Neural Network (CNN) that fuses the feature maps in both encoding and decoding sub-networks to generate a more reasonable density map and estimate the number of people more accurately. Furthermore, we introduce a new evaluation method named the Patch Absolute Error (PAE) which is more appropriate to measure the accuracy of a density map. The extensive experiments on several existing public crowd counting datasets demonstrate that our approach achieves better performance than the current state-of-the-art methods. Lastly, considering the cross-scene crowd counting in practice, we evaluate our model on some cross-scene datasets. The results show our method has a good performance in cross-scene datasets.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Crowd Density Estimation Using Fusion of Multi-Layer Features


    Contributors:
    Ding, Xinghao (author) / He, Fujin (author) / Lin, Zhirui (author) / Wang, Yu (author) / Guo, Huimin (author) / Huang, Yue (author)


    Publication date :

    2021-08-01


    Size :

    4420684 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Crowd Density Estimation via Global Crowd Collectiveness Metric

    Ling Mei / Mingyu Yu / Lvxiang Jia et al. | DOAJ | 2024

    Free access

    DDAD: Detachable Crowd Density Estimation Assisted Pedestrian Detection

    Tang, Wenxiao / Liu, Kun / Shakeel, M. Saad et al. | IEEE | 2023


    Higher-order SVD analysis for crowd density estimation

    Zhou, B. / Zhang, F. / Peng, L. | British Library Online Contents | 2012


    An Efficient Crowd Density Estimation Algorithm Through Network Compression

    Li, Meng / Chen, Tao / Li, Zhihua et al. | Springer Verlag | 2020


    An Efficient Crowd Density Estimation Algorithm Through Network Compression

    Li, Meng / Chen, Tao / Li, Zhihua et al. | TIBKAT | 2020