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Thesis Advisor唐明
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Name工程硕士
Degree Discipline计算机技术
Keyword多目标跟踪 深度学习 数据关联 跟踪漂移




Other Abstract
Multi-Object Tracking(MOT) is one of the most popular research directions in the field of computer vision. It is widely used in sport analysis,robot vision navigation, intelligent video surveillance and autonomous driving assistant systems.With the advent of the Internet Age, quantities of monitoring cameras are applied among most major public places at home and abroad, generating massive amounts of video data every second. Faced with massive video data, how to implement efficient retrieval and object tracking has become one of the difficult problems to be solved. After years of research and development, multi-target tracking technology has achieved many goals. Nevertheless, in the real world, the tracked targets are often in complex scenes such as frequent target occlusion, illumination changes, background interference and camera motion, the number of tracking targets makes it difficult for existing multi-target tracking algorithms to simultaneously satisfy high stability and real-time needs. And it is hardly to further apply in actual scenarios.
In the tracking video sequences, the appearance of the corresponding target between two consecutive frames varies slowly, therefore, the construction of the appearance model is a significant factor for the stability of tracking process. In recent years, the deep learning technology has flourished and has shown great potential in learning discriminative visual features. To tackle these difficulties of multi-target tracking algorithms in complex scenes, this thesis further considers existing algorithms, and incorporates appearance feature and motion feature extracted by deep neural networks into data association of multi-target algorithm. The main work is as follows:
(1)For the split of detection and tracking in computer vision, we implement an online multi-target tracking algorithm that integrates detection and tracking for surveillance scenarios. The algorithm detects the location of targets and extract features in the current frame, and  applies deep features with stronger expressive power to tracking. Real-time tracking can be realized in some scenaros where targets are relatively less dense;
(2)This dissertation proposes a multi-target tracking algorithm based on correlation of detection and tracking to tackle the problem that current tracking algorithms use first-order motion model or second-order motion model predict the position unreliably when a target's speed varies violently. The algorithm uses two frames of images to correlate at the feature map level, predicting the position of the target, and achieving more stable tracking;
Finally, this thesis conducts a series of experiments for evaluation. The results show that this method perform favourably against several state-of-the-art methods and achieve a leading position in some factors as well.
Subject Area模式识别
MOST Discipline Catalogue工学::计算机科学与技术(可授工学、理学学位)
Funding ProjectNational Natural Science Foundation of China[61503379] ; National Natural Science Foundation of China[61503379]
Document Type学位论文
Recommended Citation
GB/T 7714
张衡. 基于深度学习的多目标跟踪方法[D]. 北京. 中国科学院大学,2019.
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毕设论文_张衡.pdf(3920KB)学位论文 限制开放CC BY-NC-SA
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