Probabilistic neural networks based moving vehicles extraction algorithm for intelligent traffic surveillance systems

Automated vehicle detection plays an essential role in the traffic video surveillance system. Video communication of these traffic cameras over real-world limited bandwidth networks can frequently suffer network congestion or unstable bandwidth, especially in regard to wireless systems. This often hinders the detection of moving vehicles in variable bit-rate video streams. This paper presents a novel approach for vehicle detection based on probabilistic neural networks through artificial neural networks, which can accurately detect moving vehicles not only in high bit-rate video streams but also in low bit-rate video streams. The overall results of detection accuracy analyses demonstrate that the proposed approach has a substantially higher degree of both qualitative and quantitative efficacy than other state-of-the-art methods. For instance, the proposed method achieved Similarity   and F1F1 accuracy rates that were up to 61.75% and 69.38% higher than the other compared methods, respectively.