Stationary Vehicles Detection on Smart Highways and Roads using Spatiotemporal Tracking

Where traditional motorways contain hard
shoulders to provide refuge for broken-down vehicles, smart
motorways instead use live outer lane to ease congestion. The
live lanes can be closed due to accidents or breakdowns which is
communicated to other road users through overhead gantry
signs. This can only occur if the traffic management control
center is made aware of the stationary vehicle(s), through either
notification via phone call or Motorway Incident Detection and
Automatic Signaling (MIDAS) induction loop technology.
Alternatively, radar-based stopped vehicle detection is used to
identify non-moving objects on the highway. However, this
technology is unable to recognize objects or distinguish between
congestion and break down etc., which leads to generate false
alarms. For the first time, we propose a fully autonomous
computer vision and deep learning-based solution to detect
stationary vehicles on highways and local roads. We employ
deep transfer learning to build a custom-trained vehicle
detection model using a newly prepared dataset comprising over
105,000 annotated vehicle instances. DeepSort algorithm is
employed for real-time vehicle tracking through associating
instances between time series frames, followed by a rule-based
algorithm to identify the current state of detected vehicles.
Experimental outcomes show our approach as outperforming
the state-of-the-art methods in terms of efficient and reliable
detection of stationary vehicles (with 98.3% accuracy) as well as
distinguish them from congestions when evaluated over video
streams captured in realistic dynamic and diverse conditions.