  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.
