  We propose a novel online multi-target visual tracker based on the recently
developed Hypothesized and Independent Stochastic Population (HISP) filter. The
HISP filter combines advantages of traditional tracking approaches like MHT and
point-process-based approaches like PHD filter, and it has linear complexity
while maintaining track identities. We apply this filter for tracking multiple
targets in video sequences acquired under varying environmental conditions and
targets density using a tracking-by-detection approach. We also adopt deep CNN
appearance representation by training a verification-identification network
(VerIdNet) on large-scale person re-identification data sets. We construct an
augmented likelihood in a principled manner using this deep CNN appearance
features and spatio-temporal information. Furthermore, we solve the problem of
two or more targets having identical label considering the weight propagated
with each confirmed hypothesis. Extensive experiments on MOT16 and MOT17
benchmark data sets show that our tracker significantly outperforms several
state-of-the-art trackers in terms of tracking accuracy.
