With the development of intelligent transportation, an effective vehicle management method is applied to track moving vehicles with the help of monitoring equipment. Video surveillance is the main way to obtain vehicle dynamic information.
Thus, the accurate and robust vehicle tracking algorithm is urgently needed when the tracking target suffering from heavy occlusions, illumination change and scale variation.
A research team led by Prof. Dr. QIU Shi from the Xi'an Institute of Optics and Precision Mechanics (XIOPM) of the Chinese Academy of Sciences (CAS) proposed the modified Gaussian mixture model (GMM) algorithm to reduce the error judgment probability of pixel state and extract the moving target accurately.
The novel denoising auto encoder (DAE) neural network can also obtain sparse constraint in the hidden layer to limit vehicle feature model and achieve vehicle tracking accurately. The results were published in the Journal of Ambient Intelligence and Humanized Computing.
After utilizing DAE neural network and GMM moving target extraction, the tracker can be more robust to complex scenarios, including heavy occlusion, illumination change and also multiple targets.
"From the perspective of probability theory, our algorithm can reduce the probability of the error judgment of pixel state and extract the moving object better," said Prof. QIU.
The results provided a novel idea in dealing with moving target in traffic applications. The Intelligent vehicle will drive on the road all by itself in the near future.
"Our method can be one part of the whole technique which may someday help accomplish the autonomous driving," said QIU.
The flow chart of the proposed networks framework and algorithm (Image by XIOPM)
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