Urban Illegal On-road Parking Detection Algorithm for High Dynamic Video Scenarios

The increasing parking conflicts have led to serious parking violations on urban roads,posing a huge safety hazard to urban traffic.Therefore,timely and effective monitoring and handling of illegal parking events is essential to ensure urban traffic safety.However,existing illegal parking monitoring methods based on manual patrolling and fixed-point surveillance cameras have disadvantages such as low efficiency and limited monitoring range,which makes it difficult to meet the demand for large-scale urban monitoring.As an emerging sensing paradigm,vehicular crowdsensing can provide promising opportunities for large-scale and low-cost urban parking monitoring by motivating users to collect road videos while driving and upload them to the cloud.However,the complexity of in-vehicle video scenes,which leads to a high loss of vehicle target tracking and high complexity of parking judgment,poses a serious challenge to achieving accurate illegal on-road parking detection.To solve the above challenges,we propose an urban illegal on-road parking detection algorithm for high dynamic video scenarios.Specifically,first,we obtain vehicle image information across video frames through multi-vehicle target tracking on in-vehicle videos,Then,we convert the target vehicle image information into relative distance changes in real scenes through dynamic visual ranging and integrate it with the inter-vehicle movement to achieve the judgment of illegal parking.Finally,the performance of the proposed algorithm is evaluated based on the road dataset in Chongqing City.Experimental results show that the proposed algorithm achieves a detection accuracy of 87.1% for illegal parking vehicles,which is 21.9% higher than three baselines on average,and it shows excellent detection performance in different illegal parking scenarios.