  Tracking of multiple objects is an important application in AI City geared
towards solving salient problems related to safety and congestion in an urban
environment. Frequent occlusion in traffic surveillance has been a major
problem in this research field. In this challenge, we propose a model-based
vehicle localization method, which builds a kernel at each patch of the 3D
deformable vehicle model and associates them with constraints in 3D space. The
proposed method utilizes shape fitness evaluation besides color information to
track vehicle objects robustly and efficiently. To build 3D car models in a
fully unsupervised manner, we also implement evolutionary camera
self-calibration from tracking of walking humans to automatically compute
camera parameters. Additionally, the segmented foreground masks which are
crucial to 3D modeling and camera self-calibration are adaptively refined by
multiple-kernel feedback from tracking. For object detection/classification,
the state-of-the-art single shot multibox detector (SSD) is adopted to train
and test on the NVIDIA AI City Dataset. To improve the accuracy on categories
with only few objects, like bus, bicycle and motorcycle, we also employ the
pretrained model from YOLO9000 with multi-scale testing. We combine the results
from SSD and YOLO9000 based on ensemble learning. Experiments show that our
proposed tracking system outperforms both state-of-the-art of tracking by
segmentation and tracking by detection.
