|Place of Conferral||北京|
|Keyword||目标跟踪 部件上下文模型 相关滤波 在线聚类 协同跟踪|
Object tracking is a fundamental problem in computer vision. It has not only become an important bridge between object detection and video high-level behavioral semantic in organizing video structure, but also provides a significant basis for perceiving environment and action control with decision in advanced artificial intelligence. Therefore, there are many applications including intelligent monitoring, video synopsis, human-computer interaction, automatous driving, where object tracking plays an important role.
In general, a typical object tracking system consists of four components: object initialization, appearance modeling, motion estimation, and object location. It is still challenging in handling complex object appearance changes caused by factors such as illumination variation, heavy occlusion, deformation, fast motion, background clutter, \etc. Focusing on the main problems of each module in the object tracking system, the paper proposes relative models and tracking algorithms based on designing and learning object appearance visual representation, multiple parts statistical modeling, collaborative tracking with detector and tracker inspired by prior knowledge and online clustering analysis for assisting on decision. Specifically, to handle the facing problems in tracking algorithms, the paper proposes several solutions to improve the tracking performance significantly. Our works and contributions could be summarized as:
(1). Feature distilled tracking. There is a contradiction in time complexity between extracting features using very deep models and object tracking. Because extracting features using very deep models is too expensive in time cost for real-time object tracking. To alleviate this problem, we propose an ensemble method of model compress, feature extraction and scale estimation. In the process of model compression, we propose a novel method with teacher-student paradigm. Specifically, the paper proposes a small feature distilled network for visual tracking by imitating the intermediate representations of a much deeper network. The feature distilled network extracts rich visual features with higher speed than the original deeper network. To further speed-up, the paper introduces a shift-and-stitch method to reduce the arithmetic operations, while preserving the resolution of the distilled feature maps unchanged. Finally, a scale adaptive discriminative correlation filter is learned on the distilled feature for visual tracking to handle the target appearance variation. Experiments on public object visual tracking benchmarks OTB-50 and OTB-100 have showed that compared with the state-of-the-art deep tracking algorithm, the proposed method achieves the comparable performance but much $5$ times faster running speed than the original network.
(2).Part context learning for object tracking. Context information is widely used in computer vision for tracking arbitrary objects. The paper first utilizes Examplar-SVM to explore some representative parts and proposes a unified part context learning framework that can effectively capture spatial-temporal relations, prior knowledge and motion consistency to enhance the tracker's performance by overcoming the deficiency in appearance modeling. Firstly, the proposed part context tracker analyzes the interrelated information in hierarchical layers from feature, part and object. Secondly, by introducing hierarchical context graph model structure, we explore the intrinsic relation between parts and object in the tracking process, including each part in the object or the context region. Experiments indicate context structure relations are important for boosting the tracking performance with a gain of 4 percent compared with other trackers in success plots.
(3). Collaborative correlation tracking. How to handle the model drift caused by long-term occlusion orout-of-view is still an open problem. The paper proposes a collaborative correlation tracker to deal with the above problems. Firstly, the paper designs MC-HOG feature by exploring gradient and color information. Then a novel long-term detection filter with random sampling for detection is learned efficiently with random sampling to alleviate model drift by detecting effective object candidates in the collaborative tracker. In this way, the proposed approach could estimate the object state accurately by handling the model drift problem effectively. Experimental results show that the proposed method has about five percentage in performance.
(4). Clustering ensemble correlation tracking. A key problem in visual tracking is how to effectively solve the decision ambiguities of target appearances with online model update. The paper addresses this problem by incorporating sequential clustering and ensemble methods into the tracking system. In this paper, clustering is used for mining the potential historical structure in the parameter space and feature space. Then the paper fuses multiple weak hypotheses to construct a strong ensemble learner for effective object tracking. Dense experiments show that the proposed method alleviates the model drift problem by exploring group structure with online clustering and boosts the tracking performance.
|朱贵波. 基于特征学习和模型集成的目标跟踪[D]. 北京. 中国科学院大学,2016.|
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