Adaptive convolutional layer selection based on historical retrospect for visual tracking. Issue 3 (4th March 2019)

• Record Type: Journal Article
• Authors: Tang, Fuhui; Lu, Xiankai; Zhang, Xiaoyu; Luo, Lingkun; Hu, Shiqiang; Zhang, Huanlong
• Is Part Of: IET computer vision. Volume 13:Issue 3(2019)
• Page Start: 345
• Page End: 353
• Publication Date: 2019-03-04
• Subjects: object detection -- video signal processing -- cellular neural nets -- feature extraction -- learning (artificial intelligence) -- image representation -- target tracking -- object tracking -- image sequences; invariable aspect -- scene variation -- convolutional layer selection method -- robust appearance representation -- different convolutional layers -- tracking prediction -- historical retrospect -- layer-wise selection criteria -- visual object tracking -- tracker performs -- adaptive convolutional layer selection -- visual tracking -- great advance -- convolutional neural network -- CNN-based trackers -- single layer -- multiple layers; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5194
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Visual tracking has recently gained a great advance with the use of the convolutional neural network (CNN). Usually, existing CNN‐based trackers exploit the features from a single layer or a certain combination of multiple layers. However, these features only characterise an object from an invariable aspect and cannot adapt to scene variation, which limits the performance of such trackers. To overcome this limitation, the authors study the problem from a new perspective and propose a novel convolutional layer selection method. To obtain robust appearance representation, they investigate the advantages of features extracted from different convolutional layers. To determine the correctness of the tracking prediction and updated model, they design a verification mechanism based on historical retrospect, which can estimate the deviation for each layer by bidirectionally locating the target. Meanwhile, the deviation works as the layer‐wise selection criteria. Extensive evaluations on the OTB‐2013, visual object tracking (VOT)‐2016 and VOT‐2017 benchmarks demonstrate that the proposed tracker performs favourably against several state‐of‐the‐art trackers.