BBA-net: A bi-branch attention network for crowd counting
This addresses crowd counting accuracy for applications like surveillance and public safety, but it is incremental as it builds on existing CNN and attention mechanisms.
The paper tackled the problem of inaccurate crowd counting due to missing location information in CNN-based methods by proposing BBA-NET, which separately estimates density and location using a bi-branch attention network, achieving lower counting errors on two public datasets.
In the field of crowd counting, the current mainstream CNN-based regression methods simply extract the density information of pedestrians without finding the position of each person. This makes the output of the network often found to contain incorrect responses, which may erroneously estimate the total number and not conducive to the interpretation of the algorithm. To this end, we propose a Bi-Branch Attention Network (BBA-NET) for crowd counting, which has three innovation points. i) A two-branch architecture is used to estimate the density information and location information separately. ii) Attention mechanism is used to facilitate feature extraction, which can reduce false responses. iii) A new density map generation method combining geometric adaptation and Voronoi split is introduced. Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map. Extensive experiments performed on two public datasets show that our method achieves a lower crowd counting error compared to other state-of-the-art methods.