Automatic estimation of the number of people in unconstrained crowded scenes is a challenging task and one major difficulty stems from the huge scale variation of people. In this paper, we propose a novel Deep Structured Scale Integration Network (DSSINet) for crowd counting, which addresses the scale variation of people by using structured feature representation learning and hierarchically structured loss function optimization. Unlike conventional methods which directly fuse multiple features with weighted average or concatenation, we first introduce a Structured Feature Enhancement Module based on conditional random fields (CRFs) to refine multiscale features mutually with a message passing mechanism. In this module, each scalespecific feature is considered as a continuous random variable and passes complementary information to refine the features at other scales. Second, we utilize a Dilated Multiscale Structural Similarity loss to enforce our DSSINet to learn the local correlation of people's scales within regions of various size, thus yielding high-quality density maps. Extensive experiments on four challenging benchmarks well demonstrate the effectiveness of our method. Specifically, our DSSINet achieves improvements of 9.5% error reduction on Shanghaitech dataset and 24.9% on UCF-QNRF dataset against the state-of-the-art methods. * Corresponding author is Liang Lin.

It is difficult to extract meaningful patterns from massive trajectory data. One of the main challenges is to characterise, compare and generalise trajectories to find overall patterns and trends. The major limitation of existing methods is that they do not consider topological relations among trajectories. This research proposes a graph-based approach that converts trajectory data to a graph-based representation and treats them as a complex network. Within the context of vehicle movements, the research develops a sequence of steps to extract representative points to reduce data redundancy, interpolate trajectories to accurately establish topological relationships among trajectories and locations, construct a graph (or matrix) representation of trajectories, apply a spatially constrained graph partitioning method to discover natural regions defined by trajectories and use the discovered regions to search and visualise trajectory clusters. Applications with a real data set shows that our new approach can effectively facilitate the understanding of spatial and spatiotemporal patterns in trajectories and discover novel patterns that existing methods cannot find.

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