Stationary Vehicles Detection on Smart Highways and Roads using Spatio-temporal 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.