Image segmentation for intelligent video surveillance of air traffic controllers
Accurate detection and segmentation of air traffic controller instances from surveillance video images is the basis for behavior recognition and condition monitoring of individual controllers.To solve the problem of low detection and segmentation accuracy and poor robustness of air traffic controllers in complex scenes,a controller image segmentation model ATC Mask R-CNN based on Mask Region-based Convolution Neural Networks(Mask R-CNN)is proposed.Firstly,using the actual surveillance video of an air traffic control office for one week as the data source,the ATC Monitor Image Dataset(AMID)was constructed for model training and testing.Secondly,the Bottleneck Attention Module(BAM)is introduced in the backbone network to enhance the feature extraction of controllers.This module constructs a hierarchical attention mechanism similar to the human perception process by integrating spatial attentionbranches and channel attention branches.Further,low-level image features such as background textures are denoised.And to improve the detection and segmentation of occluded targets,the improved Soft Non-Maximum Suppression(Soft-NMS)algorithm was adopted instead of the NMS algorithm for candidate region selection.Compared withthe NMS algorithm,the improved Soft-NMS algorithm adopts a new intersection-union ratio calculation method and sets an attenuation function for the suggestion box where the intersection-union ratio is greater than the threshold.Therefore,adjacent occlusion targets can be avoided from being mistakenly deleted.Finally,the controller image segmentation experiment is carried out based on AMID.By selecting relevant evaluation indicators to evaluate the performance of the model,the precision,recall,and average precision of ATC Mask R-CNN are 96.49％,95.62％,and 88.84％,respectively,which proves the effectiveness of the proposed method.The experimental results show that compared with Mask R-CNN,the proposed method effectively reduces the adverse effects of complex scenes,is more suitable for controller work scenarios,and can provide technical support for the application of safety management automation in the air traffic control office.
safety engineeringintelligent video surveillancecomplex scenariosair traffic controllerinstance segmentation
NETL
NSTL
万方数据
维普
