Research On Key Techniques Of Incremental Object Tracking

Current society has already entered into information society. With the emergence of applications in video such as video surveillance, video retrieval, human-computer interaction and video encoder-decoder, there exists a huge mass of video data. The development of computer vision provides technical supports for video processing. Resorting to technology like object detection and tracking, computers are able to analyze and recognize motion states and behaviors of objects automatically, which makes video processing smarter. At present, thanks to efforts at long-term study of object tracking, the technology of object tracking makes great progress, but it is still difficult to track an arbitrary object in complex environments. As a core of framework for object tracking, appearance model has a direct influence on performances of object tracking. Based on raw information extracted from image sequences in the video, appearance model determines the possible image regions of the object in the video, but changes in viewpoints and illumination, occurences of partial occlusion all lead to deviations in object tracking, even that the object gets lost.It is key for object tracking to establishing an adaptive appearance model. Inspired by subspace learning and supervised learning, an online appearance model is contructed, which can be updated incrmentally. Several algorithms for object tracking based on the appearance model are devised to impliment tracking. The main contributions of the dissertation are summarised as follows:(1) A method for object tracking based on incremental weighted subspace learning was proposed. A low-dimensional subspace is constructed from a set of object image patches. In the subquent frames, a group of image patches are sampled, and then they are projected into the low-dimensional subspace. Image pathces are reconstructed from these projections in the subspace. Differences between original image patches and image patches reconstructed measure the likelihoods of their being the object image patch. The image patch with minimal difference is regarded as object region. Based on the new object image, the appearance model is updated incrementally. Taking time into consideration, each sample is assigned with a weight relevant to time, which makes subspace describe the appearance of the object more precisely. The experiments on the object tracking under viewpoint changes, variations in illumination and partial occlusion verify the steady and accuracy of the method.(2) A method for object tracking based on incremental non-negative matrix factorization was proposed. At first, non-negative matrix factorization is utilized to establish a low-dimensional subspace for describing appearances of objects, and each image sample is described as the linear combination of a set of image bases. Due to strong associations in the low-dimensional coordinates of object image patches, the object regions in the subsequent frames can be determined from the object regions in the previous frames. An image patch with the strongest association is selected as the object region in the current frame. After the determination of the object region, the image bases are updated incrementally in order to adjust the entire subspace online. Finally, the results of tracking the rigid object and non-rigid object prove the characteristics of the method.(3) A method for object tracking based on incrmental discriminative linear subspace was proposed. A set of image patches labelled as object and background are used to construct one-dimension discriminative subspace. Image patches in the subsequent frames are projected into the discriminative subspace, and the distance between the projection of each image patch and the centroid of the cluster of object images is computed. The distance measures the likelihood of each image patch as the object image. The image patch with the maximal likelihood is regarded as the object region in the current frame. After finishing tracking object, the spanning sets of the between-class scatter matrix and total scatter matrix are computed. Based on these sufficient spanning sets, the projection matrix is updated to maintain the discriminative power of the linear subspace. The experiments on object tracking show that the method can accomplish affination-invariant tracking.(4) A method for object tracking based on incremental asymmetric Boosting was proposed. A strong classifier is constructed based on the principle of Boosting algorithm, which contributes to classifications of the image patches sampled from the frames. Object regions are determined through the classification. To online update the appearance model of the object, pools is set up, each of which contains many weak classifiers. After identifying the object region in the current frame, every weak classifier is updated incementally. A weak classifier with minimal error is selected from a pool, and then all weak classifiers selected are combined to construct the strong classifier. Besides that, distribution of weights of training samples is adjusted to overcome asymmetry in quantity of the samples, which improves the detection of objects in the video. Finally, experiments on tracking different objects prove the generalization of the method.