Enhanced 3D convolutional networks for crowd counting
This work addresses the need for more accurate crowd and vehicle counting in video surveillance, representing an incremental improvement by incorporating temporal dependencies into existing CNN methods.
The paper tackled the problem of ignoring temporal information in video-based crowd counting by proposing a novel architecture called 'temporal channel-aware' (TCA) block, which achieved state-of-the-art performance on three benchmark datasets and beat previous methods by large margins on a vehicle dataset.
Recently, convolutional neural networks (CNNs) are the leading defacto method for crowd counting. However, when dealing with video datasets, CNN-based methods still process each video frame independently, thus ignoring the powerful temporal information between consecutive frames. In this work, we propose a novel architecture termed as "temporal channel-aware" (TCA) block, which achieves the capability of exploiting the temporal interdependencies among video sequences. Specifically, we incorporate 3D convolution kernels to encode local spatio-temporal features. Furthermore, the global contextual information is encoded into modulation weights which adaptively recalibrate channel-aware feature responses. With the local and global context combined, the proposed block enhances the discriminative ability of the feature representations and contributes to more precise results in diverse scenes. By stacking TCA blocks together, we obtain the deep trainable architecture called enhanced 3D convolutional networks (E3D). The experiments on three benchmark datasets show that the proposed method delivers state-of-the-art performance. To verify the generality, an extended experiment is conducted on a vehicle dataset TRANCOS and our approach beats previous methods by large margins.