Deformable Object Tracking with Gated Fusion

Abstract

The tracking-by-detection framework receives growing attentions through the integration with the Convolutional Neural Networks (CNNs). Existing tracking-by-detection based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. Extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against state-of-the-art methods.

References in corpus (8)

• High-Speed Tracking with Kernelized Correlation Filters
• Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
• Learning Spatially Regularized Correlation Filters for Visual Tracking
• A Novel Performance Evaluation Methodology for Single-Target Trackers
• MatConvNet - Convolutional Neural Networks for MATLAB
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Cited by in corpus (4)

• A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets
• Integration of Regularized l1 Tracking and Instance Segmentation for Video Object Tracking
• VrdONE: One-stage Video Visual Relation Detection
• Tracking by Joint Local and Global Search: A Target-aware Attention based Approach