  Multi-Object Tracking (MOT) is a challenging task in the complex scene such
as surveillance and autonomous driving. In this paper, we propose a novel
tracklet processing method to cleave and re-connect tracklets on crowd or
long-term occlusion by Siamese Bi-Gated Recurrent Unit (GRU). The tracklet
generation utilizes object features extracted by CNN and RNN to create the
high-confidence tracklet candidates in sparse scenario. Due to mis-tracking in
the generation process, the tracklets from different objects are split into
several sub-tracklets by a bidirectional GRU. After that, a Siamese GRU based
tracklet re-connection method is applied to link the sub-tracklets which belong
to the same object to form a whole trajectory. In addition, we extract the
tracklet images from existing MOT datasets and propose a novel dataset to train
our networks. The proposed dataset contains more than 95160 pedestrian images.
It has 793 different persons in it. On average, there are 120 images for each
person with positions and sizes. Experimental results demonstrate the
advantages of our model over the state-of-the-art methods on MOT16.
