  This paper introduces a novel approach to the task of data association within
the context of pedestrian tracking, by introducing a two-stage learning scheme
to match pairs of detections. First, a Siamese convolutional neural network
(CNN) is trained to learn descriptors encoding local spatio-temporal structures
between the two input image patches, aggregating pixel values and optical flow
information. Second, a set of contextual features derived from the position and
size of the compared input patches are combined with the CNN output by means of
a gradient boosting classifier to generate the final matching probability. This
learning approach is validated by using a linear programming based multi-person
tracker showing that even a simple and efficient tracker may outperform much
more complex models when fed with our learned matching probabilities. Results
on publicly available sequences show that our method meets state-of-the-art
standards in multiple people tracking.
