Research On Visual Object Tracking Modeling And Algorithm In Complex Scene

Object tracking is one of the fundamental vision tasks that tries to figure out instances of several object classes from videos and images.This task has attracted a large number of attentions as it could provide the basic semantic information for numerous applications,including safety automotive systems,robotics and intelligent video surveillance.In real object tracking scenarios,the most challenges come from variations of viewpoints,illumination occlusions,non-rigid deformations and huge intra-class variations.Despite the significant progress of existing tracking methods in the last decade,object tracking remains challenging in many computer vision applications since the appearance of the object can change significantly due to pose variations,illumination changes,shape deformations,and abrupt motions.In this thesis,we address these object tracking challenges by building a hybrid neural networks based tracker to learn powerful features by taking full advantage of learning.Specifically,this thesis makes the following contributions:First,we best exploit the self-organizing map and adaptive correlation filters for for tracking failure and recovery in complex scenarios.Building upon the recent success of self-organizing maps(SOM)and correlation filter based tracking algorithm,we have extended it along several important directions.We address tracking failure dilemma by using three correlation filters that collaborate to capture the the long-term memory of the target objects appearance.In addition to the commonly used SOM features,we propose to learn correlation filters for improving targets localization accuracy.We also optimize the learning rates to improve the performance of tracking system.We explicitly handle tracking failures by incrementally learning an online detector to recover the targets.Second,based on the rich hierarchical features of the convolutional neural network,we propsose an effective visual tracking method aiming at the problem of object tracking accuracy caused by the limitation of traditional manual feature model in complex scene.Study found that the early convolutional layers include more fine-grained spatial details,which are good for precise target locating,and the last convolutional layers of convolutional neural networks remain more semantic information of target objects,which are useful for track the objects with significant appearance variations.Both layer features with semantic information and fine-grained details are exploited simultaneously for object tracking in our method.We train correlation filter on each convolutional layer and predict the target position with a coarse-to-fine searching approach.Third,we propose deformable deep convolutional neural networks to deal with the serious occlusion and scale variation of multi-target tracking in complex scenes.The new deep learning object tracking framework has innovations in multiple aspects.To make the tracking framework lighter and incorporate temporal cues,we propose a novel frame-pair based CNNs architecture.In our proposed new deep architecture,new added deformation constrained convolution and ROI-pooling layers can model the deformation of object parts with geometric constraint and penalty.With the idea of augmenting the special sampling locations in the convolution layers and pooling layers with additional offsets that are learned from the previous feature maps,both modules can easily replace their common counterparts in regular deep CNNs.Kalman Filter is used to create tracklets association that save the location and the trajectories of tracked targets,which can help us develop a more robust tracking system.These methods reduce the size of the system,improve the speed of object tracking online,and enhance the modeling ability of geometric transformation.To sum up,in this thesis,we explicitly addressed several key challenges for robust visual tracking.Combined with memory of correlation filters,SOM network is used for feature extraction and dimensionality reduction for tracking robustness and performance.Combining the deep network layer features and correlation filters,a novel tracking algorithm is proposed for the limitation of traditional manual feature model and the low tracking accuracy.In addition,the deformable convolutional neural network is introduced for multi-target tracking.Extensive experimental results on large-scale benchmark datasets demonstrate that the proposed methods in this thesis perform favorably against state-of-the-art tracking algorithms in term of effectiveness,efficiency and robustness.