Research On Object Detection And Tracking Under Small UAV Video System

Target detection and tracking are important research topic in UAV video navigation system.The traditional target detection algorithm can not meet the target detection requirements under the complex environment of UAV video navigation system limited by the poor descriptive ability.In recent years,the development of artificial intelligence provides a new breakthrough for the performance improvement of target detection and tracking in UAV video navigation system.Deep learning is introduced to improve the target detection performance under the UAV video navigation system.Meanwhile,to cope with the poor tracking speed of traditional target tracking algorithm under the UAV video navigation system,this paper studies the improvement method based on the kernelized correlation filtering algorithm.The main work of this paper includes the following aspects:1)Research on Improvement of Target Detection in UAV Video Inspection System.Considering the high moving speed and changeable characteristic of UAV video,a two-stage pedestrian detection method based on Graph-based Visual Saliency(GBVS)and Deep Learning is proposed.First of all,according to the characteristic that the pedestrian target is mostly upright,orientation and color features of the graph-based visual saliency model are modified.Also,the final saliency map is obtained by weighted summation instead of a direct sum of feature channels.Therefore pedestrian candidate region is obtained,realizing the rough detection of first stage.Then,based on tranfer learning method,we propose a lightweight Convolution Neural Network(CNN)for fine detection.The structrue is based on depthwise separable convolutions which is a form of factorized convolutions which factorize a standard convolution into a depthwise convolution and a 1×1 convolution called pointwise convolution to realize the similar effect with the standard convolution.The experiment results show that the improved detection method gain a better performance than other one-stage CNN based detection method in tiny target detection accuracy under the UAV system due to the coarse positioning introduced.2)Research on Improvement of Target Tracking method in UAV Video Inspection System.For the situation of pedestrian target existing more deformation and scale changes in UAV video,a modified tracking method combines HOG and color feature is adopted based on the KCF.First,the color histogram features that are less dependent on the target spatial structure are introduced in order to cope with the poor robustness of target deformation with KCF.Secondly,training an independent scale filter for robust scale estimation.The results show that the improved kernelized correlation filter tracking is robust to minor deformation and occlusion,and can achieve adaptive scale changing and meet the real-time requirements.