1. Learning multiple instance deep quality representation for robust object tracking
- Author
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Wei Lo, Chun-Ming Yang, Guan Wang, and Jing Liu
- Subjects
Computer Networks and Communications ,Computer science ,business.industry ,Window (computing) ,020206 networking & telecommunications ,02 engineering and technology ,Object (computer science) ,Visualization ,Set (abstract data type) ,Recurrent neural network ,Hardware and Architecture ,Feature (computer vision) ,Video tracking ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,Representation (mathematics) ,business ,Software - Abstract
Robustly tracking various objects within a video stream with complex objects and backgrounds is a useful technique in next generation computer vision systems. However, in practice, it is difficult to design a successful video-based object tracking system due to the varied light conditions, possible occlusions, and fast-moving objects. In this work, a novel weakly-supervised and quality-guided visual object tracking model is proposed, wherein the key is a bidirectional long short-term memory recurrent neural network (BLSTM-RNN) that captures the feature sequence and predicts the quality score of each candidate window. More specifically, given a rich set of training videos annotated with the target objects, a weakly-supervised learning algorithm is first used to project all the candidate window features onto the semantic space. Next, we propose a two-stage algorithm to select the key frames from the video sequences, where both the shallow and deep filtering operations are conducted. Subsequently, the so-called BLSTM-RNN is proposed to characterize the feature sequence temporally, based on which the maximally possible object window can be calculated and finally output. In our experiment, a large video dataset containing 2045 NBA regular seasons and playoff basketball games was compiled. Based on this, a comparative study is conducted between the proposed algorithm and state-of-the-art video tracking methods. Extensive visualization results and comparative tracking precisions show the competitiveness of the proposed method.
- Published
- 2020
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