  Defining a multi-target motion model, which is an important step of tracking
algorithms, can be very challenging. Using fixed models (as in several
generative Bayesian algorithms, such as Kalman filters) can fail to accurately
predict sophisticated target motions. On the other hand, sequential learning of
the motion model (for example, using recurrent neural networks) can be
computationally complex and difficult due to the variable unknown number of
targets. In this paper, we propose a multi-target filtering and tracking
algorithm which learns the motion model, simultaneously for all targets, from
an implicitly represented state map and performs spatio-temporal data
prediction. To this end, the multi-target state is modelled over a continuous
hypothetical target space, using random finite sets and Gaussian mixture
probability hypothesis density formulations. The prediction step is recursively
performed using a deep convolutional recurrent neural network with a long
short-term memory architecture, which is trained as a regression block, on the
fly, over "probability density difference" maps. Our approach is evaluated over
widely used pedestrian tracking benchmarks, remarkably outperforming
state-of-the-art multi-target filtering algorithms, while giving competitive
results when compared with other tracking approaches: The proposed approach
generates an average 40.40 and 62.29 optimal sub-pattern assignment (OSPA)
errors on MOT15 and MOT16/17 datasets, respectively, while producing 62.0%,
70.0% and 66.9% multi-object tracking accuracy (MOTA) on MOT16/17, PNNL Parking
Lot and PETS09 pedestrian tracking datasets, respectively, when publicly
available detectors are used.
