An Approach Focusing on the Convolutional Layer Characteristics of the VGG Network for Vehicle Tracking

Abstract

Object tracking is a key technology in the field of intelligent transportation. To solve the partial occlusion problem in vehicle tracking, this paper analyzes the characteristics of a VGG convolutional neural network by experimental observation and describes its characteristics: (1) feature maps can be used for positioning, but they have redundancies, and (2) different layers of feature maps have different characteristics. After these characteristics are applied to vehicle tracking, a vehicle tracking algorithm is designed. For a given target vehicle, feature maps are generated on convolutional layers conv4_4 and conv5_4 of the VGG network, and the feature maps most relevant to the target vehicle are selected. These feature maps are used to capture target vehicle information and distinguish the target vehicle from backgrounds with similar appearances. The experiments use vehicle data from the LaSOT, VOT2017 and OTB2015 datasets to compare the vehicle tracking results of our proposed algorithm with those of other algorithms. The results show that the method proposed in this paper has certain advantages. According to algorithm implementation and vehicle tracking experiments, the proposed vehicle tracking method can solve the drift problem and is better than the traditional method at addressing drift problems.