Dense Vehicle Counting Estimation via a Synergism Attention Network

Abstract

Along with rising traffic jams, accurate counting of vehicles in surveillance images is becoming increasingly difficult. Current counting methods based on density maps have achieved tremendous improvement due to the prosperity of convolution neural networks. However, as highly overlapping and sophisticated large-scale variation phenomena often appear within dense images, neither traditional CNN methods nor fixed-size self-attention transformer methods can implement exquisite counting. To relieve these issues, in this paper, we propose a novel vehicle counting approach, namely the synergism attention network (SAN), by unifying the benefits of transformers and convolutions to perform dense counting assignments effectively. Specifically, a pyramid framework is designed to adaptively utilize the multi-level features for better fitting in counting tasks. In addition, a synergism transformer (SyT) block is customized, where a dual-transformer structure is equipped to capture global attention and location-aware information. Finally, a Location Attention Cumulation (LAC) module is also presented to explore the more efficient and meaningful weighting regions. Extensive experiments demonstrate that our model is very competitive and reached new state-of-the-art performance on TRANCOS datasets.

SyFormer: Structure-Guided Synergism Transformer for Large-Portion Image Inpainting

“…The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24) Jin et al 2022). In contrast to earlier approaches those depend heavily on hand-crafted priors (Xu et al 2021(Xu et al , 2022, deep learning-based methods have demonstrated great potential in extracting hierarchical representations from images.…”

Utilizing Image Processing and the YOLOv3 Network for Real-Time Traffic Light Control

“…In [6],we developed a new vehicle counting technique using a synergism attention network (SAN) to improve overlapping phenomena and sophisticated large-scale variations that occur in high-density images. Tey showed that the new SAN model obtains better performance indicators than MCNN, P2PNet, and DMCount.…”