Vehicle Recognition In Forest Region Based On Video Big Data

Forest resources are an important ecological foundation for human society.Therefore,the management and protection of forest resources are of great significance.The supervision of wood transport is a way of effectively alleviating the destruction of forest resources.With the popularization of video surveillance,the road bayonet in each major forest region is generally equipped with video monitoring equipment for monitoring illegal wood transport.However,the massive video data from forest road bayonet monitoring make it impossiable to be effectively handled with manual methods.With the progress of computer vision and video analysis recently,the massive video data now can possibly be handled by carefully designed algorithms.In this paper,the key algorithms and techniques for vehicle recognition in forest region based on massive video data are studied,which includes the detection of moving vehicles,the image features of the vehicle in forest region and related vehicle recognition algorithms,and deep learning method for vehicle recognition in forest region.In the study of moving vehicle detection methods,the paper first presents a review for the common methods and then proposes an improved Vibe algorithm to improve the accuracy of moving target detection and suppress the shadow area in the detection of moving vehicles.The method utilizes an improved update strategy for sample sets,and dynamic threshold to extract the foreground image.Comparing with the Gaussian mixture model and unimproved Vibe algorithm,the experiment results show that the improved Vibe algorithm has a better vehicle detection effect.In the study of features and recognition algorithms for vehicles in the forest region,the paper,with a investigation of image features of vehicles in the forest region,proposes a vehicle recognition method based on YCbCr color features and Hough transform circle detection.The method first obtains the region of interest(wood region)from vehicle image with image color based segmentation,and then to detect the existence of circles for the region of interest with Hough transform to finally identify the wood transport vehicle.In an effort to further improve recognition accuracy,the paper also studies forest vehicle recognition method based on deep learning which uses the unsupervised convolutional autoencoder to extract the features of wood transport vehicles autonomously.The study also focuses on the imbalance of video data from the road bayonet,and successfully solve the data imbalance with XGBoost integrated classifier for vehicle classification and identification.The experiments suggest that deep learning based method presents better recognition result in term of accuracy for the vehicle recognition in the forest region.