WAEF: Weighted Aggregation with Enhancement Filter for Visual Object Tracking
| Autor: | Litu Rout, Deepak Mishra, Rama Krishna Sai Subrahmanyam Gorthi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science 020207 software engineering Pattern recognition 02 engineering and technology Filter (signal processing) Tracking (particle physics) Convolutional neural network Feature (computer vision) Video tracking 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030110086 ECCV Workshops (1) |
| DOI: | 10.1007/978-3-030-11009-3_4 |
| Popis: | In the recent years, convolutional neural networks (CNN) have been extensively employed in various complex computer vision tasks including visual object tracking. In this paper, we study the efficacy of temporal regression with Tikhonov regularization in generic object tracking. Among other major aspects, we propose a different approach to regress in the temporal domain, based on weighted aggregation of distinctive visual features and feature prioritization with entropy estimation in a recursive fashion. We provide a statistics based ensembler approach for integrating the conventionally driven spatial regression results (such as from ECO), and the proposed temporal regression results to accomplish better tracking. Further, we exploit the obligatory dependency of deep architectures on provided visual information, and present an image enhancement filter that helps to boost the performance on popular benchmarks. Our extensive experimentation shows that the proposed weighted aggregation with enhancement filter (WAEF) tracker outperforms the baseline (ECO) in almost all the challenging categories on OTB50 dataset with a cumulative gain of 14.8%. As per the VOT2016 evaluation, the proposed framework offers substantial improvement of 19.04% in occlusion, 27.66% in illumination change, 33.33% in empty, 10% in size change, and 5.28% in average expected overlap. |
| Databáze: | OpenAIRE |
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