  Traffic flow analysis is revolutionising traffic management. Qualifying
traffic flow data, traffic control bureaus could provide drivers with real-time
alerts, advising the fastest routes and therefore optimising transportation
logistics and reducing congestion. The existing traffic flow datasets have two
major limitations. They feature a limited number of classes, usually limited to
one type of vehicle, and the scarcity of unlabelled data. In this paper, we
introduce a new benchmark traffic flow image dataset called TrafficCAM. Our
dataset distinguishes itself by two major highlights. Firstly, TrafficCAM
provides both pixel-level and instance-level semantic labelling along with a
large range of types of vehicles and pedestrians. It is composed of a large and
diverse set of video sequences recorded in streets from eight Indian cities
with stationary cameras. Secondly, TrafficCAM aims to establish a new benchmark
for developing fully-supervised tasks, and importantly, semi-supervised
learning techniques. It is the first dataset that provides a vast amount of
unlabelled data, helping to better capture traffic flow qualification under a
low cost annotation requirement. More precisely, our dataset has 4,402 image
frames with semantic and instance annotations along with 59,944 unlabelled
image frames. We validate our new dataset through a large and comprehensive
range of experiments on several state-of-the-art approaches under four
different settings: fully-supervised semantic and instance segmentation, and
semi-supervised semantic and instance segmentation tasks. Our benchmark dataset
will be released.
