  Compressive Sensing, which offers exact reconstruction of sparse signal from
a small number of measurements, has tremendous potential for trajectory
compression. In order to optimize the compression, trajectory compression
algorithms need to adapt compression ratio subject to the compressibility of
the trajectory. Intuitively, the trajectory of an object moving in starlight
road is more compressible compared to the trajectory of a object moving in
winding roads, therefore, higher compression is achievable in the former case
compared to the later. We propose an in-situ compression technique underpinning
the support vector regression theory, which accurately predicts the
compressibility of a trajectory given the mean speed of the object and then
apply compressive sensing to adapt the compression to the compressibility of
the trajectory. The conventional encoding and decoding process of compressive
sensing uses predefined dictionary and measurement (or projection) matrix
pairs. However, the selection of an optimal pair is nontrivial and exhaustive,
and random selection of a pair does not guarantee the best compression
performance. In this paper, we propose a deterministic and data driven
construction for the projection matrix which is obtained by applying singular
value decomposition to a sparsifying dictionary learned from the dataset. We
analyze case studies of pedestrian and animal trajectory datasets including GPS
trajectory data from 127 subjects. The experimental results suggest that the
proposed adaptive compression algorithm, incorporating the deterministic
construction of projection matrix, offers significantly better compression
performance compared to the state-of-the-art alternatives.
