  Region of Interest (ROI) crowd counting can be formulated as a regression
problem of learning a mapping from an image or a video frame to a crowd density
map. Recently, convolutional neural network (CNN) models have achieved
promising results for crowd counting. However, even when dealing with video
data, CNN-based methods still consider each video frame independently, ignoring
the strong temporal correlation between neighboring frames. To exploit the
otherwise very useful temporal information in video sequences, we propose a
variant of a recent deep learning model called convolutional LSTM (ConvLSTM)
for crowd counting. Unlike the previous CNN-based methods, our method fully
captures both spatial and temporal dependencies. Furthermore, we extend the
ConvLSTM model to a bidirectional ConvLSTM model which can access long-range
information in both directions. Extensive experiments using four publicly
available datasets demonstrate the reliability of our approach and the
effectiveness of incorporating temporal information to boost the accuracy of
crowd counting. In addition, we also conduct some transfer learning experiments
to show that once our model is trained on one dataset, its learning experience
can be transferred easily to a new dataset which consists of only very few
video frames for model adaptation.
